Characterization of miR-574-5p decoy to CUGBP1 in human ......Emmerich, Anne Caterina: Characterization of miR-574-5p decoy to CUGBP1 in human lung cancer cells using a mass spectrometry

Characterization of miR-574-5p decoy to

CUGBP1 in human lung cancer cells using a

mass spectrometry proteomics approach

Vom Fachbereich Biologie der Technischen Universität Darmstadt

Zur Erlangung des akademischen Grades

Doctor rerum naturalium

(Dr. rer. nat.)

Dissertation von

M. Sc. Anne Caterina Emmerich

Erstgutachterin: Dr. Meike Julia Saul

Zweitgutachterin: Prof. Dr. Beatrix Süß

Darmstadt 2020

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Emmerich, Anne Caterina: Characterization of miR-574-5p decoy to CUGBP1 in human lung

cancer cells using a mass spectrometry proteomics approach

Darmstadt, Technische Universität Darmstadt

Jahr der Veröffentlichung der Dissertation auf TUprints: 2020

URN: urn:nbn:de:tuda-tuprints-91406

Tag der mündlichen Prüfung: 28.02.2020

Veröffentlicht unter CC BY-SA 4.0 International

https://creativecommons.org/licenses/

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In der Wissenschaft gleichen wir alle nur den Kindern, die am Rande des Wissens hie und da einen Kiesel

aufheben, während sich der weite Ozean des Unbekannten vor unseren Augen erstreckt.

Sir Isaac Newton (1643 – 1727)

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Table of Contents

Summary ...............................................................................................................................9

Zusammenfassung .............................................................................................................10

1. Introduction .................................................................................................................12

1.1 Post-transcriptional mechanisms of gene regulation ...................................................12

1.1.1 Alternative splicing................................................................................................14

1.1.2 RNA-binding proteins (RBPs) ...............................................................................16

1.1.2.1 CUGBP1 and the CELF family of RBPs .........................................................18

1.1.3 MicroRNAs (miRs) ................................................................................................21

1.1.3.1 Canonical miR functions ................................................................................23

1.1.3.2 Non-canonical miR functions ..........................................................................24

1.1.3.3 MiR-574-5p ....................................................................................................25

1.2 mPGES-1-derived PGE2 in cancer development .........................................................27

1.2.1 Regulation of mPGES-1 by the miR-574-5p/CUGBP1 decoy mechanism in human

lung cancer....................................................................................................................29

1.3 Aim of the study ..........................................................................................................30

2. Materials and Methods ................................................................................................31

2.1 Cell culture methods ...................................................................................................31

2.1.1 Cell culture conditions ..........................................................................................31

2.1.2 Depletion of CUGBP1 using RNA interference .....................................................31

2.1.3 Overexpression of miR-574-5p .............................................................................32

2.1.4 Depletion of miR-574-5p by LNA™ inhibitors ........................................................32

2.1.5 Wound healing assay ...........................................................................................33

2.1.6 Trans-well migration assay ...................................................................................33

2.2 RNA methods .............................................................................................................34

2.2.1 RNA extraction .....................................................................................................34

2.2.2 mRNA or miR quantification by qRT-PCR.............................................................34

2.2.3 RNA immunoprecipitation (RIP) ............................................................................36

2.3 Protein methods ..........................................................................................................38

2.3.1 Soluble and microsomal fraction preparation ........................................................38

2.3.2 Determination of protein concentration .................................................................38

2.3.3 SDS-PAGE and Western Blot ...............................................................................38

2.3.4 TMT labelling and mass spectrometry ..................................................................39

2.4. Bioinformatical methods .............................................................................................40

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2.4.1 3’UTR analysis .....................................................................................................40

2.4.2 Mass Spectrometry data analysis .........................................................................41

2.4.3 Ingenuity pathway analysis (IPA) ..........................................................................41

2.5 Fluorescent labeling techniques ..................................................................................42

2.5.1 Immunostaining ....................................................................................................42

2.5.2 Fluorescence in situ hybridization (FISH) .............................................................42

2.6 Microscopy and image acquisition...............................................................................43

2.6.1 Immunostaining and FISH images ........................................................................43

2.6.2 Wound healing assay images ...............................................................................43

2.7 Statistics .....................................................................................................................43

3. Results .........................................................................................................................44

3.1 Verification of CUGBP1 binding via RIP ......................................................................44

3.1.1 Establishment of CUGBP1 RIP protocol ...............................................................44

3.1.2 CUGBP1 binds to miR-574-5p and mPGES-1 mRNA ...........................................45

3.2. TMT-based proteomics study of IL-1β-stimulated A549 cells .....................................46

3.2.1 Proteome changes in A549 upon ΔCUGBP1, ΔmiR-574-5p and miR-574-5p oe ..48

3.2.2 Validation of TMT proteomics study using Western blot analysis ..........................52

3.3 Physiological impact ...................................................................................................55

3.3.1 Pathway analysis predicts canonical pathways, upstream regulators and biological

functions ........................................................................................................................55

3.3.2 Influence of miR-574-5p and mPGES-1 on migratory behavior of A549 cells .......56

3.4 Identification of new CUGBP1 targets .........................................................................57

3.4.1 Western blot analysis of microsomal proteins upon ΔCUGBP1 ............................57

3.4.2 Binding of CUGBP1 to mRNAs of novel canonical targets ....................................58

3.5 Identification of novel miR-574-5p/CUGBP1 decoy targets .........................................59

3.5.1 Investigating a “decoy regulation pattern” via Western blot analysis .....................59

3.5.2 Stringent “decoy regulation pattern” in the proteomics study ................................62

3.5.3 Binding analysis of potential decoy targets ...........................................................63

3.6 Subcellular localization of CUGBP1 and miR-574-5p in A549 cells .............................64

3.7 Bioinformatical analysis of 3’UTR splicing patterns .....................................................65

4. Discussion ...................................................................................................................69

4.1 Insights into the proteome of A549 lung cancer cells ..................................................69

4.2. Discovery and verification of new canonical CUGBP1 targets ....................................70

4.3 Decoy target search ....................................................................................................72

4.4 Bioinformatical 3’UTR analysis revealed unique splice pattern ....................................73

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4.5 Physiological impact ...................................................................................................74

4.5.1 Influence of miR-574-5p on metastasis .................................................................74

4.5.2 Influence of mPGES-1 on metastasis ...................................................................75

4.6 Outlook .......................................................................................................................76

References ..........................................................................................................................78

Appendix .............................................................................................................................96

Abbreviations ....................................................................................................................96

Supplementary data ..........................................................................................................99

Curriculum vitae .............................................................................................................. 104

Ehrenwörtliche Erklärung ................................................................................................ 106

Danksagungen ................................................................................................................ 107

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List of Figures

Figure 1: mRNA processing..................................................................................................13

Figure 2: Different types of AS. .............................................................................................15

Figure 3: General structure of a GU-AG intron......................................................................16

Figure 4: Interaction of RBPs with RNAs. .............................................................................17

Figure 5: General structure of the CELF family members. ....................................................19

Figure 6: miR biogenesis. .....................................................................................................22

Figure 7: Canonical targets regulated by miR-574-5p. ..........................................................26

Figure 8: Prostanoid biosynthesis. ........................................................................................28

Figure 9: Regulation of mPGES-1 gene expression via the miR-574-5p/CUGBP1 decoy

mechanism. ..........................................................................................................................29

Figure 10: Boyden chamber set-up. ......................................................................................34

Figure 11: Validation of RIP protocol. ...................................................................................45

Figure 12: CUGBP1 binding to mPGES-1 mRNA and miR-574-5p in RIP assays. ...............46

Figure 13: Quantification of CUGBP1 knockdown. ...............................................................47

Figure 14: TMT based proteomics approach. .......................................................................48

Figure 15: Expected regulations in the proteomics study. .....................................................49

Figure 16: Numbers of proteins differentially expressed upon ΔCUGBP1, ΔmiR-574-5p or miR-

574-5p oe in soluble and microsomal fraction of the proteomics study. ................................51

Figure 17: Proteomics validation using Western blot analysis. ..............................................54

Figure 18: Top ten regulated biological processes predicted by IPA. ....................................56

Figure 19: Migration assays..................................................................................................57

Figure 20: Protein levels of potential CUGBP1 targets in IL-1β-stimulated A549 cells with

manipulated CUGBP1 levels. ...............................................................................................58

Figure 21: Binding of CUGBP1 to potential new target mRNAs. ...........................................59

Figure 22: Schematic overview of the decoy mechanism. ....................................................60

Figure 23: Investigating a “decoy regulation pattern” via Western blot analysis. ...................61

Figure 24: Proteins with a stringent “decoy regulation pattern” in the proteomics study. .......63

Figure 25: Binding of CUGBP1 to potential decoy targets.....................................................63

Figure 26: Subcellular localization of CUGBP1 and miR-574-5p in A549 cells......................64

Figure 27: High stringency and low stringency approach in bioinformatical 3‘UTR analysis. .66

Figure 28: Transcripts from low stringency 3’UTR analysis. ..................................................68

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List of Tables

Table 1. Comparison of different GRE clusters. ....................................................................19

Table 2. PCR program for mRNA quantification ...................................................................35

Table 3. Primer used for qRT-PCR .......................................................................................35

Table 4. PCR program for miR quantification .......................................................................36

Table 5. RIP buffer composition ...........................................................................................37

Table 6. Gel composition for SDS-PAGE..............................................................................39

Table 7. Primary antibodies for Western blot analysis ..........................................................39

Table 8. Numbers of increased (↑) and decreased (↓) proteins in each fraction and each

condition of the proteomics study compared to their respective controls...............................50

Table 9. Potential canonical miR-574-5p targets. .................................................................52

Table 10. List of potential binding motifs of CUGBP1 ...........................................................65

Table 11. Number of transcripts fulfilling the low stringency analysis criteria. .......................67

Table 12. Top three upregulated proteins of the proteomics study. .......................................99

Table 13. Top three downregulated proteins of the proteomics study. ..................................99

Table 14. IPA prediction of top five canonical pathways. .................................................... 100

Table 15. IPA prediction of top five upstream regulators. .................................................... 100

Table 16. Summary of all analyzed proteins. ...................................................................... 101

Table 17. Transcripts from bioinformatical 3’UTR analysis ................................................. 103

Summary __________________________________________________________________________

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Summary

The bioactive lipid mediator prostaglandin (PG) E2 is generated by the enzyme microsomal

prostaglandin E2 synthase-1 (mPGES-1). Especially in lung tumors, mPGES-1 was shown to

be significantly overexpressed which contributes to a pro-tumorigenic microenvironment.

Current medication interfering with the negative effects of PGE2 comprise only non-steroidal

anti-inflammatory drugs (NSAIDs). While these have effective analgesic properties and are

commonly used as pain killers, treatment of tumor growth is still inconclusive. Probably, only

subgroups of cancer patients exhibit an abnormal prostanoid profile. Therefore, a reliable

biomarker is necessary to identify patients who could benefit from said treatment. In a recent

study, it was discovered that a specific microRNA (miR) can induce mPGES-1 gene

expression. The miR-574-5p prevents binding of the inhibitory CUG-RNA binding protein 1

(CUGBP1) to the 3´ untranslated region (UTR) of mPGES-1. This non-canonical decoy

function of miR-574-5p leads to an increased mPGES-1 protein level. Following, an induction

of PGE2 formation triggers the progression of lung tumor growth in vivo. Interestingly, the entire

influence on tumor progression could be blocked with the addition of a specific mPGES-1

inhibitor, confirming the huge influence of miR-574-5p on (patho-) physiological mPGES-1

functions. In this study, a proteomics approach was conducted in order to further characterize

this decoy mechanism in human lung cancer cells. The aim was to gather global insights into

the proteome changes related to miR-574-5p and CUGBP1, especially in a compartment

specific manner. Further, it was aimed to identify new CUGBP1 targets and find out if they are

also affected by the decoy function of miR-574-5p. Two new CUGBP1 targets were validated

herein: NADH-Ubiquinone Oxidoreductase Core Subunit S2 (NDUFS2) and Mothers against

decapentaplegic homolog 2 (SMAD2). However, both NDUFS2 and SMAD2 are independent

from miR-574-5p levels. In a bioinformatical 3’UTR analysis of potential CUGBP1 targets, it

was shown that the specific splicing pattern of mPGES-1 is unique, comprising two long

CUGBP1 binding motifs with a 3’UTR intron in between. Only 11 other transcripts harbor a

similar but not identical pattern in their sequence. Hence, it is assumable that this novel decoy

mechanism is specifically regulating mPGES-1 in A549 lung cancer cells. This might be caused

by the unique splice pattern of mPGES-1. However, further experiments are needed to confirm

this hypothesis. Nevertheless, specificity of the decoy mechanism would open up new

opportunities for lung cancer patients. By using miR-574-5p as a biomarker, one could stratify

those patients with high mPGES-1 levels who have a higher chance to benefit from the anti-

tumorigenic potential of NSAID therapy.

Zusammenfassung __________________________________________________________________________

10

Zusammenfassung

Der biologisch aktive Lipidmediator Prostaglandin (PG) E2 wird enzymatisch von der

mikrosomalen Prostaglandin E2 Synthase (mPGES-1) generiert. Es konnte gezeigt werden,

dass mPGES-1 speziell in Tumoren der Lunge stark angereichert ist. Durch die damit

verbundene vermehrte Bildung von PGE2 kommt es zu einer kanzerogenen Tumorumgebung.

Hier setzen sogenannte non-steroidal anti-inflammatory drugs (NSAIDs) an. Während diese

effektive analgetische Eigenschaften aufweisen und häufig als Schmerzmittel genutzt werden,

ist die Behandlung von Tumoren dennoch umstritten. Es wird vermutet, dass nur eine

Subpopulation von Krebspatienten ein entartetes Prostanoidprofil aufweist. Daher wäre ein

verlässlicher Biomarker vonnöten, mit dem man diese Patienten identifizieren kann, die dann

von einer NSAID-Therapie profitieren könnten. Kürzlich wurde eine spezielle mikroRNA (miR)

identifiziert, die die mPGES-1 Genexpression induzieren kann. Die miR-574-5p verhindert die

Bindung des inhibitorischen CUG RNA Bindeprotein 1 (CUGBP1) an den 3‘ untranslatierten

Bereich (UTR) von mPGES-1. Diese nicht-kanonische Decoy Funktion von miR-574-p führt zu

einem erhöhten mPGES-1 Proteinlevel. Dadurch kommt es zur vermehrten PGE2 Synthese,

welche daraufhin das Tumorwachstum in vivo begünstigt. Durch die zeitgleiche Gabe eines

spezifischen mPGES-1 Inhibitors konnte jedoch der gesamte Einfluss auf das Voranschreiten

des Tumors verhindert werden. Dadurch konnte der immense Einfluss der miR-574-5p auf die

(patho-) physiologischen Funktionen von mPGES-1 gezeigt werden. In dieser Studie wurde

nun der Decoy Mechanismus mit Hilfe einer Proteomik-Studie in humanen

Lungenkarzinomzellen weiter charakterisiert. Das Ziel war es globale Kompartment-

spezifische Einsichten in die miR-574-5p- und CUGBP1-vermittelten Änderungen des

Proteoms zu erhalten. Des Weiteren sollten neue CUGBP1-regulierte Transkripte identifiziert

werden, um herauszufinden, ob diese ebenfalls durch den Decoy Mechanismus der

miR-574-5p beeinflusst werden. Zwei neue CUGBP1-regulierte Transkripte konnten dabei

validiert werden: die NADH-Ubiquinone Oxidoreductase Core Subunit S2 (NDUFS2) sowie

das Signalmolekül Mothers against decapentaplegic homolog 2 (SMAD2). Die Regulation

beider Zielgene war jedoch unabhängig von miR-574-5p. In einer bioinformatischen Analyse

aller 3’UTRs von möglichen CUGBP1-regulierten Transkripten stellte sich heraus, dass das

spezifische mPGES-1 Spleißmuster einzigartig ist. Es umfasst zwei lange CUGBP1

Bindesequenzen, getrennt durch ein 3’UTR Intron. Ein ähnliches, wenn auch nicht identisches

Muster, konnte in nur 11 weiteren Transkripten gefunden werden. Daher ist es annehmbar,

dass der Decoy Mechanismus spezifisch nur die mPGES-1 Expression in A549

Lungenkarzinomzellen reguliert. Dies könnte potenziell auf das spezifische Spleißmuster

zurückzuführen sein, obwohl weitere Experimente nötig sind, um diese Hypothese zu

bestätigen. Nichtsdestotrotz könnte die Spezifität des Decoy Mechanismus neue

Zusammenfassung __________________________________________________________________________

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Möglichkeiten für Lungenkrebspatienten darstellen. MiR-574-5p könnte als Biomarker genutzt

werden, um jene Patienten mit hohen mPGES-1 Leveln zu identifizieren. Diese könnten dann

von den inhibitorischen Effekten einer NSAID-Behandlung auf das Tumorwachstum

profitieren.

Introduction __________________________________________________________________________

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1. Introduction

Transcription is one of the most fundamental processes in biological systems. It reliably

produces the required amounts of RNA which are necessary for the maintenance of general

cellular functions but also for the reaction to rapid changes of environmental circumstances. In

multicellular organisms, every individual cell shares the same genome, however expressing a

characteristic cell type specific protein profile. To orchestrate this kind of specificity, a

tremendous amount of regulatory action is required. In fact, various post-transcriptional

regulation mechanisms are responsible for fine-tuning of the intracellular protein repertoire [1].

1.1 Post-transcriptional mechanisms of gene regulation

Post-transcriptional regulation can occur at any step of mRNA processing, from transcription

to translation, both within the nucleus as well as in the cytoplasm [2]. Shortly, mRNA

processing starts as soon as transcription is initiated by Polymerase II [3]. Afterwards, pre-

mRNAs are capped, spliced, edited and polyadenylated. Then, mature mRNAs are exported

into the cytoplasm through the nuclear pore complex [4]. In the cytoplasm, mRNAs can be

translated into proteins, stored in stress granules, processing bodies (P-bodies) or they can be

marked for degradation (see Figure 1).

All these steps are mediated by a myriad of factors, most of them RNA-binding proteins (RBPs)

with some of them binding the pre-mRNA even during transcription [5]. Regulatory

mechanisms include among others interference with splicing, editing or polyadenylation as well

as mRNA (de-) stabilization, localization and finally translational inhibition [2]. For instance,

polyadenylation is a critical step as it stabilizes the mRNA molecule and prevents rapid

degradation in the cytoplasm [6]. The longer the 3’ poly(A)-tail, the longer the mRNA can

survive in the cytoplasm, where it gradually gets shorter by deadenylation [7]. Initiation of

translation however, stops further deadenylation which indicates that poly(A)-shortening is a

mechanism of regulation. The deadenylation is mediated by the poly(A) ribonuclease (PARN)

[8]. Recruitment of PARN is thereby mediated by binding of RBPs or miRs [9] [10] [11] [12].

Another example for a regulatory mechanism is mRNA storage in intracellular particles called

P-bodies. mRNAs get recruited there by interaction with RBPs or miRs [13]. P-bodies were

described to play a role in all kinds of mRNA decay mechanisms, however it was also

demonstrated that mRNAs were temporary stored there for later translation [13] [14] [15].

Introduction __________________________________________________________________________

13

Figure 1: mRNA processing. Genes consists of a promoter region directly upstream of the transcription start site (TSS), numerous exons and introns as well as untranslated regions at the 5’ and 3’ end. The DNA is transcribed by Polymerase II, generating the precursor mRNA (pre-mRNA). Processing includes addition of a 5’ 7-methylguanylate (5’ m7G) cap, polyadenylation of the 3’ end (An 3’) as well as splicing to remove intronic sequences. All these processing steps can concurrently occur during transcription although a consecutive order is indicated in the figure for facilitation purpose. Mature mRNA is exported into the cytoplasm where its fate is influenced by localization, degradation or successful translation. Modified from [14] [15].

Post-transcriptional regulation is mainly based on cis-regulatory elements (CRE) [18]. In this

context, CREs are defined as regions of non-coding DNA or RNA that provide binding sites for

trans-acting factors such as RBPs or transcription factors. CREs can be upstream or

downstream of coding sequences (CDS) or even within introns and are mostly termed

Introduction __________________________________________________________________________

14

enhancers or silencers, depending on their regulatory function [19]. Of note, one CRE can be

bound by numerous trans-acting factors and vice versa, which is called pleiotropy. Moreover,

interactions, synergisms or competition of various trans-acting factors as well as the

combination of activating or inhibitory CREs further elevates the complexity of gene expression

regulation [18]. In the following chapters, three types of post-transcriptional regulation

mechanisms are described in more detail as they stand in the focus of this thesis (see chapters

1.1.1 Alternative Splicing, 1.1.2 RNA-binding proteins (RBPs) and 1.1.3 microRNAs (miRs)).

1.1.1 Alternative splicing

Alternative splicing (AS) is a fine tuning process of higher eukaryotes which enables a variation

in the in- or exclusion of sequence parts of a pre-mRNA and thus provides greater biodiversity

of proteins from an established number of genes [20]. There are several estimations towards

how many of all transcripts are alternatively spliced which range up to 25% in Caenorhabditis

elegans (C. elegans), 60% in Drosophila melanogaster [21] and even 90% in humans [22] [23].

Investigations concerning different human tissues revealed that roughly 50% of alternatively

spliced isoforms are differentially expressed among tissues indicating that AS also provides

cell type specific isoforms [24]. However, there are recent publications questioning the impact

of AS on protein diversity, as apparently many RNA isoforms could not be found on protein

level in large scale proteomics studies [19] [20]. Nevertheless, this does not render the fact

that AS is a pivotal process in regard of post-transcriptional regulation and physiological

homeostasis. Defects in AS can even cause different diseases most of all cancer development

and progression [27].

During the splicing process it is decided which parts of the sequence are included in the mature

mRNA and which ones are removed [28]. Consequentially, several mature mRNAs can result

from one pre-mRNA. There are distinct types of AS (see Figure 2). Constitutive splicing

describes the canonical form including one exon after the other and removing all the intronic

parts. One variant is exon skipping, which describes the case when one exon is cut out

together with the adjacent introns. Vice versa, an intron can also be included in the mature

mRNA. Further, there can be mutually exclusive exons, also called cassette exons. Finally, the

5’ and 3’ splice sites can vary. All these variations lead to different mRNAs and thereby also

to different amino acid sequences during translation [29].

Introduction __________________________________________________________________________

15

Figure 2: Different types of AS.

Colored boxes indicate exons; thin lines in between resemble introns. The different forms of AS can lead to distinct mature mRNAs which also influences the amino acid sequence of the proteins. Modified from [30].

The large complex which mediates the splicing process is called spliceosome. It comprises

over 300 proteins and nucleic acids [31] [32], while the core is composed of five small nuclear

ribonucleoproteins (snRNPs) U1-2, 4-6 [33]. Furthermore, additional trans-acting factors of the

heterogeneous nuclear ribonucleoprotein (hnRNP) [34] or Serine/arginine (SR) protein family

[35] act as repressors or activators by binding to silencer or enhancer regions to regulate

splicing activity depending on the cell type, the developmental stage or the gene [36].

Generally, the spliceosome binds to splice sites and cuts out the intronic sequences. Thereby,

assembly and dissociation of the spliceosome subunits appear periodically and recur for every

intron [37]. Thus, the splicing process can be divided in three parts: assembly of the

spliceosome, catalytic splicing (actual removal of the intron) and recycling of the snRNPs [29].

The decision which part of the pre-mRNA is an exon and which is intron depends on a number

of cis-elements. Conserved cis-elements adjacent to splicing sites are called splicing acceptor

sites. They are found on exon-intron-boundaries of pre-mRNAs and are often UG-rich such as

UUCUG and UGUU [38] [39].

Generally, introns consist of a 5’ donor site, a branch point and a 3‘ acceptor site. Apart from

the few self-splicing introns [40], most introns need a spliceosome to be cut out. There are two

types of introns: the most common type of intron is processed by the so-called major

Introduction __________________________________________________________________________

16

spliceosome and has a 5’ GU and a 3’ AG, whereupon the GU is strictly conserved and

surrounded by a less conserved sequence (see Figure 3). The much more uncommon AU-AC

type intron is processed by the minor spliceosome [41].

Figure 3: General structure of a GU-AG intron.

Framed by two exons, the intron starts with a 5’ splice site that contains a conserved GU within a less conserved sequence. The branch point containing a highly conserved A is followed by a pyrimidine rich region and finally the AG comprising 3’ splice site. Modified from [41] [42]. Py: Pyrimidine nucleobase (cytosine or uracil)

Interestingly, splicing not only occurs in the CDS of pre-mRNAs but also in UTRs. Removal of

introns obviously has a tremendous impact on the length. Shorter 3'UTR isoforms have fewer

binding sites for trans-acting factors such as miRs and are consequently more stable, resulting

in higher protein level [43]. Higher expression rates in turn are linked to proliferating cells.

Therefore, it is no surprise that shorter 3’UTRs can often be found in oncogenes and are

associated with carcinogenic cells [44]. Nevertheless, it is often thought that 3’UTR splicing

inevitable leads to nonfunctional transcripts due to nonsense-mediated decay (NMD).

However, this is only the case when the intron is less than 55 nucleotides (nt) from a

termination codon resulting in a pre-mature stop codon [45] [46]. Splicing within UTRs still does

not gain much attention, although estimations on how many human UTRs contain introns range

from 35% in 5’UTRs [47] to 6-16% in 3’UTRs [48] [49].

In general, AS has a crucial impact on all kinds of cellular functions. Dysregulation can even

lead to variances in cell cycle control, proliferation or apoptosis [50]. Therefore, it is discussed

in literature to announce AS an additional hallmark of cancer [51]. Especially genomic splice

site point mutations seem to be affected. For instance, there are at least 29 different splice site

mutations in the Tumor Protein P53 (TP53/p53) gene that are found in all kinds of tumor types

including lung cancer, breast cancer and leukemia [52]. It is well described that during normal

differentiation oncogenes are inactivated via AS, whereas in tumor cells AS is manipulated to

inactivate tumor suppressors [53] [54] [55] [56]. This underlines the importance of AS and post-

transcriptional regulation in general for physiological and cellular homeostasis.

1.1.2 RNA-binding proteins (RBPs)

RBPs fulfill a crucial role in post-transcriptional gene expression. Generally, it is proposed that

unbalanced expression and function of RBPs occurs in the context of uncontrolled cell

Introduction __________________________________________________________________________

17

proliferation and promotes tumor growth [57]. This reflects the relevance of protein-RNA

interactions in cellular homeostasis. However, a definite causal connection has not yet been

described [58] [59].

In fact, RBPs control stability, decay, translation as well as localization of mRNAs (see Figure

4A). They are able to shuttle mRNAs between the nucleus and the cytoplasm to actively

translating ribosomes, stress granules or P-bodies [4] [60] [61] [62] [63] [64]. Vice versa, RBPs

can also be the target of regulation by RNAs rather than being a regulating factor. The

discovery of long non-coding RNAs (lncRNAs) and their association with the organization,

scaffolding or inhibition of protein arrangement made it clear that RNA can also act on its bound

protein, which contradicts the general view that it is normally the other way round. Thereby,

RNAs can have an impact on localization, stability, interactions or functions of a protein (see

Figure 4B) [65].

Figure 4: Interaction of RBPs with RNAs.

Functional crosstalk of RBP/RNA interaction can occur in both directions. (A) RBPs can bind to RNAs via RNA-binding domains, influencing various aspects of the RNAs functions and fate. (B) By displaying protein-binding activity, certain RNAs (e.g. lncRNA) can affect various protein functions. Modified from [65].

The formation of a RNA-Protein-complex, also called ribonucleoprotein (RNP) complex is

mediated by specific RNA-binding domains (RBDs) [66] [67]. Thereby, different specificities

and affinities are based on sequence and structure of the RNA target. RBDs can be classified

as followed: RNA-recognition motifs (RRMs), double stranded RBDs or Zinc finger domains

[4]. Those RBDs can then bind to CREs mostly in 3’UTRs of mRNAs.

One of the best described CREs are probably AU-rich elements (AREs). They are ubiquitously

found in 3’UTRs of mRNAs [68] and interact with a variety of RBPs which can tag the RNA for

Introduction __________________________________________________________________________

18

rapid degradation [69] but also stabilization [70]. In recent years, a similar sequence element

called GU-rich element (GRE) was discovered. GREs are highly conserved throughout

evolution and were primarily found in 3’UTRs of mRNAs with short half-lives [71]. Generally,

GU-rich sequences appear in ca. 5% of RNAs in the human transcriptome [72]. They can

regulate splicing, translation, deadenylation or mRNA decay, depending on the RBP they

interact with during different intracellular settings [73] [74]. It was elucidated that GREs are

specifically targeted by the CELF (CUG-Binding protein and embryonically lethal abnormal

vision-type RNA binding protein like factors) family of RBPs [71].

1.1.2.1 CUGBP1 and the CELF family of RBPs

The CELF family influences a wide range of post-transcriptional processes, such as AS [75]

[76] [77], deadenylation [9], C-U editing [78] [79], transport [80] [81] [82], translation [83] and

most of all mRNA decay [84] .

The RBP family is evolutionary conserved and comprises 6 members: CELF1-6 [85] [86] [87].

All of them harbor three RRMs, two N-terminal RBDs and one in the C-terminal region [88] [89]

(see Figure 5). The divergent domain is potentially important for functional regulation but this

is still discussed throughout literature. While CELF1 (CUG-RNA binding protein 1; CUGBP1)

and CELF2 (CUGBP2) are ubiquitously expressed among cell types and tissues and fulfill a

role in embryonic development [90] [91] [92] [93], CELFs 3-6 are only expressed in fully

developed cells and are exclusively found in nervous tissue [94] [95] [88]. Although, it was

proposed several years ago that the family members have redundant functions in mRNA

regulation [96], it was later demonstrated that all have specific RNA binding affinities and

distinct functions [97]. Physiologically, CELFs are crucial regulators for all kinds of

developmental processes. This is especially well described for xenopus [98]. Besides, there

are also several mouse models describing CELF-mediated shifting from fetal to adult

alternative splice variants of several skeletal muscle transcripts [75] [99] [100]. Whereas it is

experimentally confirmed that CELFs bind to C/UG-rich splicing acceptor sites, a prediction if

this inhibits or activates AS is impossible, as it strongly depends on the cellular context [101].

Introduction __________________________________________________________________________

19

Figure 5: General structure of the CELF family members. All CELF members consist of three RRMs: two N-terminal ones and one C-terminal RRM, with a divergent domain in-between that distinguishes them. Numbers indicate amino acids. Modified from [102].

CELFs preferentially bind to 15-22 nt long GU-rich sequences [103] [104] [105] whereas, most

RBDs bind shorter (GU-rich) motifs like TAR DNA Binding Protein (TARDBP) [106]. GREs

have defined consensus sequences based on the pentameric GUUUG (see Table 1) which

was originally identified in human T-cells [71]. Today, it is known that GRE-containing

transcripts appear in a variety of cells, including other immune cells, mouse brain cells or

human cancer cells [84]. While the exact outcome mostly depends on cellular and

environmental context, GREs also transfer instability when cloned in otherwise stable

transcripts [71].

Table 1. Comparison of different GRE clusters.

GRE mRNAs were clustered (one mismatch allowed) into five subclasses based on the number of pentameric (GUUUG) repeats and surrounding sequences. K stands for G or U. Clusters I and II contain four or more overlapping GUUUG pentamers and are found in only a few hundred transcripts such as transcription factors, cell cycle regulators and intercellular communication genes. Clusters III, IV and V represent shorter sequences with less repetition and are found in several thousand transcripts. CELF: CUGBP Elav-Like Family Member; ELAVL4: Embryonic Lethal Abnormal Vision Like Neuron-Specific RNA Binding Protein 4; RBM38: RNA Binding Motif Protein 38; TARDBP: TAR DNA Binding Protein. Based on [107] and [72].

Cluster GRE sequences Functional categories Trans-acting factors

I GUUUGUUUGUUUGUUUGUUUG transcription factors, cell cycle, cell metabolism, cell–cell communication regulators

CELF1, CELF2, ELAVL4, RBM38, TARDBP, FUS

II GUUUGUUUGUUUGUUUG

III GUKUGUUUGUKUG

IV KKGUUUGUUUGKK

V KKKU/GUKUG/UKKK

Introduction __________________________________________________________________________

20

In the focus of this thesis is the CELF family member CUGBP1. It was first discovered in 1996

and described to regulate myotonic dystrophy type 1 (DM1) [108]. Initial SELEX experiments

(systematic evolution of ligands by exponential enrichment) demonstrated that CUGBP1

preferably binds GU-repeat sequences (UGU) [104]. To date, it is also described to bind to

GC-rich or even A-containing sequences [109]. CUGBP1 in general is known as an inhibitory

post-transcriptional regulator. It is responsible for mRNA deadenylation [9], subsequent

degradation [110] or AS [111] [112] and is conserved in a variety of species including humans,

mice, drosophila and xenopus [113] [114]. It was found that knockdown of CUGBP1 led to a

severe stabilization of GRE containing transcripts [115] [116] [117] which underlines its

function as gene expression repressor. In contrast to its paralogue, CUGBP2 which rather

stabilizes targets [118].

The activity of CUGBP1 is regulated via its phosphorylation status [119]. In total it has 9

described phosphorylation sites mostly on serines or threonines [120]. Through

hyperphosphorylation by Protein Kinase C, CUGBP1 is stabilized and reveals elevated splicing

activity in DM1 [121]. In mouse myoblasts, CUGBP1 is described to be phosphorylated at

serine 28 by AKT Serine/Threonine Kinase 1, which influences its function as translational

regulator during myocyte differentiation and murine heart development [122]. Finally, it was

shown that phosphorylation by cyclin D3-CDK4/6 additionally interferes with CUGBP1’s RNA

binding capacity [123]. So overall, phosphorylation seems to be one key factor for CUGBP1

regulation on many levels.

As regulator of AS, CUGBP1-mediated exon skipping or inclusion depends on the

developmental stage of the cell [112]. This function is best investigated in the context of DM1

[111]. The autosomal dominant neuromuscular disease is characterized by a trinucleotide

repeat extension in the gene for myotonic dystrophy protein kinase (DMPK) resulting in an

impaired gene expression [124]. In that regard, a balance between CUGBP1 and the splicing

factor muscle blind like protein 1 (MBNL1) is essential. Gain of CUGBP1 function goes along

with loss of function of MBNL1 which leads to AS of a variety of crucial transcripts [124]. The

outcome can include heart conduction problems, impaired muscle strength, cataract

development or insulin resistance [125]. Moreover, CUGBP1 influences ca. 50% of heart

development-related transcripts by changing the splicing events between fetal and adult

developmental stages [126].

Several promising studies with mouse models are investigating CUGBP1’s effects on cardiac

dysfunction and cardiomyopathy [126] [127]. However, CUGBP1 functions are too diverse to

distinguish between effects based on AS, mRNA degradation or deadenylation. Interestingly,

up to now it is not known how exactly CUGBP1 mediates deadenylation of human transcripts.

Concerning mammalians, it is only known that CUGBP1 recruits PARN in the human hepatoma

Introduction __________________________________________________________________________

21

cell line Huh7 [128] and cell-free assays [9]. Interaction with PARN surely does indicate

involvement of deadenylation [84] but this is still under investigation. As deadenylation is a

crucial step in degradation of mammalian transcripts [129] [130], it was consequential to

investigate if CUGBP1 is involved in other mechanisms of mRNA decay. It is postulated that

there exists some kind of CUGBP1-mediated decay process [71], but to date there are no

studies elucidating the exact mechanism. It is well studied that CUGBP1 regulates whole

networks of transcripts (regulons) involved in murine myoblast growth and differentiation,

including crucial targets associated with cell cycle and survival [110]. In addition, CUGBP1

plays an important role in the rapid alteration of expression profiles during the activation of

human T-cells through alternative polyadenylation [131]. In activated primary T-cells,

hyperphosphorylation of CUGBP1 impairs its binding to target mRNAs. The results are

increased protein levels of the targets which include a variety of proteins associated with an

activated proliferative cell type [132].

Additionally, CUGBP1 also plays a role in the regulation of translation. It has activating

properties, observed at many stages of cellular development [133] [134]. However, under

stressful conditions CUGBP1 can also act as silencer and suppress translation in conjunction

with several other proteins [135]. Additionally, the mode of action apparently depends on the

context and cell type. It was recently described that in mesenchymal cells as well as MCF-10A

breast cancer cells, CUGBP1 has a positive effect on translation of a variety of mRNAs

involved in epithelial to mesenchymal transition (EMT) [136] [137]. Whereas in intestinal

epithelial cells it represses translations of the insulin like growth factor 2 receptor mRNA.

Overall, the mechanisms of how CUGBP1 is involved in translation, deadenylation and mRNA

decay are not fully elucidated to date.

1.1.3 MicroRNAs (miRs)

MiRs are highly conserved non-coding RNAs and complete the complex network of post-

transcriptional regulators covered within this thesis. MiRs are described as short single

stranded RNAs of approximately 21 nt length. The first miR was discovered in 1993, when Lee

et al. found lin-4 in the first larval stage of the nematode C. elegans. They discovered that this

small RNA was able to repress the lin-14 gene expression by complementary binding to its

3’UTR [138]. The name “microRNA” was defined not before the year 2001, though [139].

There are several ways of miR biogenesis (see Figure 6). On the one hand, a subset of miRs

is derived from introns (mirtrons) or even exons of protein-coding genes, meaning their

expression is depending on the host gene [140] [141] [142]. On the other hand, miRs can also

by transcribed from specific miR-genes by RNA polymerase II [143] [144]. Transcription of

miR-genes creates so-called primary miRs (pri-miRs) which are further processed via splicing,

Introduction __________________________________________________________________________

22

editing and polyadenylation [144] [145]. The double stranded pri-miRs are usually several

hundred nucleotides long and form a hairpin structure [146] [147]. A large microprocessor

complex comprising the RNA binding protein DiGeorge Syndrome Critical Region 8 (DGCR8)

and the ribonuclease III Drosha further processes the pri-miR to generate the much shorter

precursor-miR (pre-miR) [146] [148] [149]. In contrast, mirtron-derived pre-miRs are generated

by the spliceosome and an additional processing step mediated by the debranching enzyme,

to form a hairpin from a single stranded intron [150].

Nuclear export of all pre-miRs is then conducted by the shuttle protein exportin-5 which

recognizes a two nt overhang at the 3’ end of the hairpin [149]. During this energy consuming

step, the Ras-related nuclear protein (RAN) provides the necessary guanosine triphosphate

(GTP) [151]. Cytosolic pre-miRs need to be further processed. Therefore, the RNA

Polymerase III Dicer is recruited [149] [152]. Enzymatically, the loop structure is removed from

the hairpin by Dicer, resulting in an 18-22 nt long miR duplex [153]. Although either strand of

the miR duplex is potentially functional, usually only one strand does fulfill physiological

functions, while the other one is degraded [147] [154].

Figure 6: miR biogenesis.

miRs can either be transcribed from specific miR genes or spliced from intronic sequences of host genes. In the ladder case, the spliceosome and a debranching enzyme are necessary to generate the pre-miR. Specific miR genes produce an intermediate molecule called pri-miR which is capped and polyadenylated and further processed by Drosha to create the pre-miR. With the help of RAN-GTP and Exportin-5, the pre-miR is shuttled to the cytoplasm where it is processed by Dicer to generate a miR duplex, which finally leads to the mature miR of 18-22 nt length. Modified from [155].

Introduction __________________________________________________________________________

23

MiRs fulfill an essential role in physiological homeostasis by regulating all kinds of cellular

functions. Thus, it is no surprise that dysregulation of miRs can cause severe impairment of

normal body functions and can lead to diseases including autoimmune conditions [156], heart

[157] [158] [159] and kidney diseases [160], hereditary diseases such as non-symptomatic

progressive hearing loss [161] and many different types of cancer [162].

Indeed, many miRs are associated with cancer development and progression and are

intensively studied in that regard. In general, miRs that are associated with cancer are called

oncomiRs [163]. This term applies to all miRs that show a differential expression level in tumor

cells, if they act as oncogenes or tumor suppressors [164]. A prominent example is miR-21,

which has been shown to be increased in most types of cancers including tumors of the colon,

lung, breast, pancreas, as well as leukemia and lymphoma [165] [166] [167]. In contrast, in

many tumors types, a global decrease in overall miR levels has been described [168] [169].

Additionally, a severe shortening of 3’UTRs was observed. Shorter 3’UTRs are beneficial

because they provide a smaller number of miR binding sites, allowing for upregulation of

oncogenes [170]. This is based on the canonical miR function as inhibitor of gene expression,

which will be explained in the following chapter.

1.1.3.1 Canonical miR functions

Conventionally, miRs are known as global gene expression repressors. They bind to 3’UTRs

of target mRNAs and impair proper translation or even lead to degradation of the mRNA. Either

mechanism leads to a decreased protein level. This mode of action is called RNA interference

(RNAi). Originally, it was described for small interfering RNAs (siRNAs) which act against viral

infections [171].

Mainly in plants, miRs show a near perfect pairing, while target recognition in animals is

mediated by a specific seed region within the miR sequence. It is located at position 2-8 on the

5’ end of the miR [172]. In most cases, the seed region base pairs with responsive elements

in the 3’UTR of the mRNA targets. Although, computational analysis reported that miR binding

sites are also found all over 5’UTRs and coding-sequences [173] [174].

The key to canonical miR functionality is the formation of the RNA-induced silencing complex

(RISC) [175]. This protein complex is formed minimally by Argonaute 2 (AGO2) [176] [177] and

the respective miR, but usually it also comprises other proteins from the AGO family [178] as

well as further RISC-associated proteins such as DEAD-box helicase 20 [179]. However, to

date the exact composition of the RISC is not yet fully understood and seems to vary between

studies.

The miR duplex is recruited to the RISC by Dicer. Within the complex, it is decided which strand

of the duplex functions as guide strand and which one is degraded by RISC [180]. Usually, the

Introduction __________________________________________________________________________

24

strand with higher 5’ end stability is the functional one [181]. The bound miR is then used to

target specific mRNAs via Watson-crick base pairing while the AGO proteins are responsible

for the mode of action of gene regulation [180]. MiRISC can influence the gene expression of

mRNA transcripts via two different mechanisms [182] [183]. Dependent on the level of

complementarity, miR binding either leads to translational repression or RISC-mediated

degradation of the mRNAs. These are the commonly known functions of miRs, however in the

last few years diverse studies described a variety of further functions and modes of action that

miRs are involved with.

1.1.3.2 Non-canonical miR functions

For a long time, miRs were only considered to bind to mRNAs and act as post-transcriptional

repressors. Only a few years ago, it was discovered that miRs can also have a variety of totally

different functions. For example, miRs which are secreted in exosomes are incorporated by

donor cells and can interact with toll-like receptor 7/8 (TLR7/8) [184]. Fabbri et al. found that

human miR-21 and miR-29a can both be secreted by tumor cells to act on human TLR8 or

murine TLR7 in adjacent immune cells and induce a pro-inflammatory immune response [184].

In the following, several studies confirmed the interaction e.g. in the context of neuroblastoma

[185], neuropathic pain [186] or murine myoblasts [187] or even in the context of Alzheimer’s

disease [188].

The discovery of this unusual miR function was accompanied by a variety of studies also

describing novel modes of actions. For instance, miRs were found to act as activator or silencer

of transcription. The first one was miR-327, which was described to induce Cadherin-1 (CDH1)

and Cold Shock Domain Containing C2 (CSDC2) in human prostate cancer cells via a

complementary promoter sequence [189]. Later, there was similar evidence for various other

miRs, however the exact mechanism is still unclear.

Furthermore, miRs can also control miR maturation. For example, Let-7 forms a positive

feedback loop by binding to its own pri-miR. In turn, further processing of the pri-miR is

enhanced which elevates the mature let-7 levels in C. elegans [190]. On the other hand,

miR-709 is able to block maturation of other miRs. It binds to a specific motif in the

miR-15a/16-1 pri-miR structure in murine cells and inhibits further processing [191]. Moreover,

miRs can also interact with other non-coding RNAs. It was found that 4% of all AGO-mRNA

tags were associated with lncRNAs, indicating that miRs could recruit AGOs to lncRNAs to

influence their stability or function [192].

This thesis focuses on the new non-canonical decoy function of miRs. Eiring et al. initially

demonstrated that miR-328 can interact with the RBP hnRNP E2 [193]. In this context, miR-328

positively influences the gene expression of CCAAT/enhancer-binding protein alpha (CEBPA)

Introduction __________________________________________________________________________

25

by acting as competitive inhibitor to hnRNP E2 in leukemic blasts. This new decoy function is

independent of the miR’s seed region and solely functions through interference with the RBP.

In 2016, it was revealed that this novel mode of action does not only influence gene expression

of CEBPA alone, but also S100A9 was validated as miR-328/hnRNP E2 decoy target in

monocytes [194]. The list was recently extended to HMGB1 as well as 141 further proteins

which were also predicted as decoy targets [195]. Hence, the miR-328 and hnRNP E2 decoy

seems to have a global impact on a variety of targets and was even observed in different cell

types.

Recently, this decoy mechanism was also described for miR-574-5p and CUGBP1 in A549

lung cancer cells [196] which will be discussed more closely in the following chapters.

1.1.3.3 MiR-574-5p

MiR-574-5p is a mirtron encoded in the first intron of the gene FAM114A1. This gene is coding

for the nervous system overexpressed protein 20 (NOXP20), which is overexpressed in the

brain [197] although NCBI GEO data [198] indicate that it might be expressed all over the body,

especially in mesenchymal cells [199]. Nevertheless, there is still very little known about the

function of NOXP20. Only the fact that it contains a caspase recruiting domain gives a hint that

it might be involved in apoptosis [197]. It is implied that miR-574-5p expression is connected

to NOXP20 expression. However, since there are no publications on the regulation of NOXP20

expression, no further conclusions can be drawn from that.

So far, only two publications describe how miR-574-5p expression is regulated. There is

evidence that NFκB transcription factor p65 could regulate its transcription in mice in a context

of neurological disorders [200]. Furthermore, it has been observed that the amyloid precursor

protein APP influences the miR-574-5p level in the development of the cerebral cortex,

although by an unknown mechanism [201].

Regulation of not the expression but rather the functionality of miR-574-5p is described more

closely in literature. There are reports of three different lncRNAs to regulate miR-574-5p. While

lncRNA-MFI2-AS1 (melanotransferrin) influences miR-574-5p in colon cancer cells [202], in

breast cancer it is lnc-Zinc Finger Protein 469 (ZNF469)-3 which binds to miR-574-5p [203]. In

papillary thyroid carcinoma cells, lnc-PTCSC3 (Papillary Thyroid Carcinoma Susceptibility

Candidate 3) is described to have an impact by binding to miR-574-5p [204]. In this case, the

interaction is further associated with Wingless-Type MMTV Integration Site Family Member 1

(Wnt) signaling. MiR-574-5p regulates β-catenin/Wnt-signaling via suppressor of cancer cell

invasion (SCAI) and thereby influenced invasion and migration of the tumor cells [204].

Indeed, the relationship between miR-574-5p and Wnt signaling is described in several other

studies. In colon and thyroid cancer, Wnt signaling was found to be activated by inhibition of

Introduction __________________________________________________________________________

26

the RBP quaking (Qki) [205] [206]. In fact, miR-574-5p targets Qki-6/7 which results in a

reduced protein level [205]. In turn, Qki no longer acts as suppressor of β-catenin and the Wnt

signaling pathway is activated [207]. Both leads to further progression of tumor growth by

facilitating proliferation and inhibiting apoptosis of the colon cancer cells. In contrast,

miR-574-5p is also described to act anti-tumorigenic in two other colorectal cancer studies by

targeting metastasis-associated in colon cancer protein 1 (MACC1) [208] or MYC Binding

Protein (MYCBP) [202].

On the other hand, the influence of miR-574-5p on lung cancer development seems to be

unambiguous. Several publications showed that miR-574-5p acts pro-metastatic and

enhances tumor progression of (non-) small cell lung cancer ((N)SCLC) by targeting among

others checkpoint suppressor 1 (CHES1) and protein tyrosine phosphatase receptor type U

(PTPRU) [209] [210] [211]. Additionally, miR-574-5p also seems to promote cell growth in the

context of coronary artery disease by binding to the mRNA of zinc finger DHHC-type containing

14 (ZDHHC14) [212]. Finally, miR-574-5p is described to repress the expression of ceramide

synthase 1 (CerS1) together with HDAC1 [213], while it can also act in concert with miR-361-5p

in white adipose tissue to regulate early B cell factor 1 (EBF1) [214].

Figure 7: Canonical targets regulated by miR-574-5p. miR-574-5p has been described to directly target a variety of mRNAs. Thereby, it influences various types of cancers, as well as cardiovascular and neurological conditions. miR-574-5p binds the mRNAs of the respective proteins, and downregulates the expression. Based on [200] [202] [204] [205] [206] [208] [209] [210] [211] [212] [213] [214].

Introduction __________________________________________________________________________

27

It gets clear that miR-574-5p plays a role in a variety of diseases and cellular processes by

direct interaction with various targets (see Figure 7). Besides the direct base pairing with a

mRNA, miR-574-5p was recently described to interact with the RBP CUGBP1 [196]. By this

mechanism, miR-574-5p has an impact on lipid metabolism and tumor progression which will

be discussed in the next two chapters.

1.2 mPGES-1-derived PGE2 in cancer development

In 2011, alteration in lipid metabolites was announced a hallmark of cancer [215]. Metabolic

reprogramming of lipid mediators obviously also includes prostanoids such as prostaglandin

E2 (PGE2). The metabolite PGE2 is a bioactive lipid mediator which shows great importance in

the regulation of a variety of physiological and pathophysiological processes, such as

inflammation, pain and tumorigenesis [216] [217] [218] [219]. It is generated in a first step by

the enzymes cyclooxygenase (COX)-1 or COX-2 by converting arachidonic acid to PGH2.This

instable intermediate is then further converted by microsomal prostaglandin E2 synthase 1

(mPGES-1) [220]. The generated PGE2 is secreted by the cell and elicits a wide range of pro-

tumorigenic functions by binding to the EP1-4 receptors. High levels of mPGES-1 could be

observed in various types of cancers including colon [221], prostate [222] and lung cancer

[223]. Increased levels of COX-2 as well as mPGES-1 are further associated with a poor overall

survival rate [224]. MPGES-1 activity does not only have an impact on pain and inflammation

in the tumor microenvironment but also enhances the progression of the tumor itself. Moreover,

PGE2 is responsible for the crosstalk of cancer and stromal cells within the tumor tissue. The

result is a highly efficient immune evasion, which facilitates further tumor growth [219]. One

example is cervical cancer-derived PGE2 which was observed to induce monocyte

differentiation into tumor-associated macrophages (TAMs) [225]. Furthermore, PGE2 not only

suppresses an anti-tumorigenic immune response but also facilitates tumor growth directly

[226]. In the case of breast cancer, PGE2 leads to lymph angiogenesis while the EP4 receptor

seems to be the most important one in that context [227]. Also in lung cancer, EP4 is the crucial

receptor, leading to enhanced tyrosine kinase c-Src activation and subsequently to an

increased tumor growth as well as metastasis [228].

Overall, there are numerous publications describing the influence of PGE2 on tumor

progression (for reviews see [218] [229] [230] [231]). Therefore, it is no surprise that the pro-

tumorigenic properties of PGE2 attracted great attention to potential pharmacological inhibition

of its synthesis. However, current approved medication to interfere with the negative effects of

PGE2 comprise only inhibitors of the COX enzymes. Especially in long time treatment, these

therapeutic approaches show severe side effects like increased cardiovascular events or

gastrointestinal bleeding [232] [233] [234] [235] [236]. With inhibition of the COXs, other

Introduction __________________________________________________________________________

28

prostanoids like PGD2, prostacyclin (PGI2) and thromboxane (TXA) are also impaired (see

Figure 08). For this reason, it would be much more reasonable to target mPGES-1 rather than

COX-2 [237]. Inhibition of mPGES-1 would minimize side effects [238] and hypothetically also

cause a shift towards PGI2 and PGD2, which would be beneficial for the cardiovascular system.

[239] [240]. Several studies have shown that genetic deletion or pharmacological inhibition of

mPGES-1 is indeed a promising tool against tumor growth [222].

Figure 8: Prostanoid biosynthesis.

Arachidonic acid is converted by either COX-1 or COX-2. The resulting instable intermediate PGH2 is then further processed by respective synthases to generate the prostanoids PGE2, PGI2, TXA2, PGD2 or PGF2a. NSAIDs inhibit COX enzymes and therefore all prostanoids downstream of PGH2. Therefore, inhibition of only PGE synthase (mPGES-1) by CUGBP1/miR-574 decoy would be more beneficial and should not affect other prostanoids. Modified from [241].

However, most of the developed mPGES-1 inhibitors have a problematic limitation: they do

not work in mouse or rat models, since mPGES-1 is not conserved in rodents [242]. As murine

and human mPGES-1 differ in three amino acids near the active site of the enzyme [243], the

majority of human inhibitors does not repress the murine enzyme activity. Therefore, any kind

of pre-clinical study is impossible, due to a lack of established animal models. An exception is

Compound III which inhibits both human and murine mPGES-1 [244] [245] [246] [247] and was

already shown in pre-clinical studies to be an efficient tool against neuroblastoma [246].

Introduction __________________________________________________________________________

29

Besides pharmacological inhibition of mPGES-1, recently a post-transcriptional regulation

mechanism was unravelled which will be described more closely in the next chapter.

1.2.1 Regulation of mPGES-1 by the miR-574-5p/CUGBP1 decoy mechanism in human

lung cancer

Recently, the PGE2-generating synthase mPGES-1 was found to be regulated in a non-

canonical way [196]. Until then, it was not much known about post-transcriptional regulation of

mPGES-1. In its 3’UTR there are two long GREs which represent binding sites for the RBP

CUGBP1. A similar sequence can be found in miR-574-5p. In an inflammatory environment

like stimulation with IL-1β, miR-574-5p is able to sequester CUGBP1 away from the mPGES-1

mRNA (see Figure 9). This eliminates the negative influence of the RBP and additionally

results in an AS event. The mPGES-1 3’UTR is spliced which removes a conserved ALU

element in between the two GREs. The shorter splice variant has a higher translational rate.

Therefore, the decoy has an overall enhancing effect on mPGES-1 gene expression [196].

Figure 9: Regulation of mPGES-1 gene expression via the miR-574-5p/CUGBP1 decoy mechanism. CUGBP1 binds to two GREs within the mPGES-1 3’UTR. Upon IL-1β simulation, miR-574-5p acts as decoy to CUGBP1, preventing it from binding to the GREs. This results in an AS event creating a shorter 3’UTR isoform with a higher translational rate. As a result, mPGES-1 levels and PGE2 synthesis are increased as well as subsequent tumor growth in vivo. Modified from [196].

Introduction __________________________________________________________________________

30

The increased mPGES-1 protein level then leads to a higher level of its enzymatic product

PGE2. As described above, PGE2 has a crucial influence on the progression of cancer. In a

xenograft mouse model, it could be demonstrated that this decoy mechanism has a

tremendous impact on lung tumor growth. When miR-574-5p overexpressing lung cancer cells

were injected into nude mice hind flanks, it was revealed that they displayed a strongly

increased proliferation. The tumor weight and volume was significantly increased compared to

control tumors. Moreover, urinary PGE-M levels linked this to enhance PGE2 formation.

Interestingly, progression of miR-574-5p overexpressing tumors was reduced back to control

level with the simultaneous administration of the mPGES-1 inhibitor Compound III. This proved

that the pro-tumorigenic effects of miR-574-5p were solely caused by the decoy-mediated

mPGES-1 induction [196]. This conclusion is consequential because a delayed growth of

murine xenograft tumors was already observed upon mPGES-1 knockdown in an earlier study

[222]. Hence, this new non-canonical mPGES-1 regulation mechanism is an intriguing

research topic in the context of future lung cancer research.

1.3 Aim of the study

With the discovery of the new decoy mechanism in human lung cancer, it would be possible

for the first time to regulate mPGES-1 expression on mRNA level. In order to further

characterize the miR-574-5p/CUGBP1 decoy in lung cancer, a TMT-based proteomics study

was used to unravel the overall impact on cellular protein levels. It should be revealed if the

miR-574-5p/CUGBP1 decoy has a global impact similar to the miR-328/hNRNP E2 decoy or

if mPGES-1 could be the only target. In that case, this would open up new options for NSCLC

patients. Not all patients benefit from a treatment with medication that aims to reduce PGE2

levels [248] [249]. Potentially, because not all lung adenocarcinomas are comparably PGE2-

dependent. Therefore, levels of miR-574-5p could be used as stratification marker in order to

identify those patients with higher mPGES-1 and PGE2 levels. For this subgroup, a treatment

with COX inhibitors could be highly beneficial in the fight against NSCLC.

Materials and Methods __________________________________________________________________________

31

2. Materials and Methods

2.1 Cell culture methods

2.1.1 Cell culture conditions

The human cell line A549 (ATCC Manassas, VA, USA) is derived from a 58-year old male with

lung adenocarcinoma. Cells were cultured in Dulbecco’s modified Eagle medium (DMEM, Life

technologies) supplemented with 10% (v/v) fetal calf serum (FCS; Life technologies),

100 U/mL penicillin (PAA the Cell Culture Company) 100 µg/mL streptomycin (PAA the Cell

Culture Company) and 1 mM sodium pyruvate (PAA the Cell Culture Company) (= fully

complemented medium). Cells were grown in T75 cell culture flasks under standard growth

conditions (humidified atmosphere of 5% CO2 at 37°C). When the cells reached a confluence

of ~ 70-90%, medium was aspirated and cells were washed with pre-warmed phosphate

buffered saline (PBS). Then, they were detached using pre-warmed Trypsin-EDTA (Invitrogen)

at 37°C for 5 min. Reaction was stopped adding pre-warmed full culture medium (1:1) to the

cells and number of viable cells was examined by trypan blue staining and counted using Bio-

Rad TC10 automated cell counter (both Bio-Rad Laboratories). Approximately 1 million cells

were transferred into a new T75 culture flask. For all experiments, cells were seeded in 6-well

plates à 5x105 cells per well in 2 mL medium. Except for RIP assays, where 3 x 106 A549 cells

were seeded in 10-cm dishes in 10 mL medium.

For preparation of liquid nitrogen stocks, A549 cells were detached as described above and

resuspended in medium containing 10% (v/v) DMSO (Carl Roth). The suspension was

transferred into cryo vials (VWR) and stored at -80°C for 2 days. Afterwards, cryo vials were

transferred into a liquid nitrogen tank until further use. In order to thaw cells again, the cryo

vials were carefully pre-warmed at room temperature until suspension began to thaw. Then,

pre warmed medium was added and the cryo vial was rinsed until the whole suspension was

thawed and transferred in a 50 mL reaction tube. Cells were precipitated in a centrifuge for 5

min at 1,200 rpm (Eppendorf Centrifuge 5702), to remove DMSO. Supernatant was discarded,

the cell pellet was resuspended in fresh pre-warmed medium and transferred in a T75 cell

culture flask.

2.1.2 Depletion of CUGBP1 using RNA interference

By using siRNA oligonucleotides, CUGBP1 was transiently knocked down. Therefore, a

previously published siRNA (5´-GCUGUUUAUUGGUAUGAUU-3´) was used. 24 h prior to

transfection, A549 cells were seeded in a 6-well plate as described above. For transfection,

20 pmol/µL siRNA oligonucleotides were transfected using Lipofectamin2000® (Invitrogen)

according to manufacturer’s instructions. A siRNA against GFP, naturally not expressed, was


32

designed (5´-UCUCUCACAACGGGCAUUU-3´) and used as negative control. After 24 h, cells

were stimulated with 5 ng/mL interleukin (IL)-1β (Sigma-Aldrich). Further 24 h later, the

samples were harvested using 500 µL pre-warmed Trypsin-EDTA (Invitrogen) as described

above. Transfection efficiency was tested by Western blot analysis (see chapter 2.3.3 SDS-

PAGE and Western Blot).

2.1.3 Overexpression of miR-574-5p

For transient overexpression (oe) of miR-574-5p, the miRIDIAN hsa-miR-574-5p mimic

(HMI0794, Sigma-Aldrich) and negative control (HMC0002, Sigma-Aldrich) were used. A549

cells were seeded 24 h prior to transfection in a 6-well plate. 20 pmol/µL per well of the mimics

or control were transfected using Lipofectamin2000® (Invitrogen) according to the

manufacturer´s instructions. After 24 h, cells were stimulated with 5 ng/mL IL-1β (Sigma-

Aldrich). Further 24 h later, the samples were harvested using 500 µL pre-warmed Trypsin-

EDTA (Invitrogen) as described above. The efficiency was assessed by qRT-PCR analysis

(see chapter 2.2.2 mRNA or miR quantification by qRT-PCR) as stated in [196].

For stable overexpression of miR-574-5p, the lentiviral particles Mission® lenti miR-574-5p

(HLMIR0794, Sigma-Aldrich) or Mission® lenti control (NCLMIR001, Sigma-Aldrich) were

used. A549 cells were seeded at a density of 5 x 105 per well in a 6-well plate 24 h prior to

transduction. Lenti viral particles were rapidly thawn and added to the cells at a MOI of 0.83

for spinoculation (875 x g, at 32°C for 60 min). Transduced cells were incubated for 24 h in

fully complemented DMEM before 10 µg/mL puromycin (Sigma-Aldrich) were added for four

days to select the transduced clones. These stable A549 miR-574-5p overexpression and

control cell lines were generously provided by Stefan Stein, Georg-Speyer Haus, Frankfurt

[196]. The transduction efficiency was verified by qRT-PCR (see chapter 2.2.2 mRNA or miR

quantification by qRT-PCR).

2.1.4 Depletion of miR-574-5p by LNA™ inhibitors

Transient depletion of miR-574-5p was achieved using LNAs™ from Exiqon (miR-574-5p-

LNA™ inhibitor and negative control MIMAT0004795). A549 were seeded 24 h prior to

transfection at a density of 5 x 105 cells in a 6-well plate. 40 pmol/µL per well was transfected

using Lipofectamin2000® (Invitrogen) according to manufacturer’s instructions. After 24 h, cells

were stimulated with 5 ng/mL IL-1β (Sigma-Aldrich). Further 24 h later, the samples were

harvested using 500 µL pre-warmed Trypsin-EDTA (Invitrogen) as described above. Efficiency

of the knockdown was measured by qRT-PCR analysis (see chapter 2.2.2 mRNA or miR

quantification by qRT-PCR) as stated in [196].


33

2.1.5 Wound healing assay

For determination of migratory behavior of A549 cells, wound healing assays were performed

with stable A549 miR-574-5p overexpression and control cells. Therefore, cells were seeded

in 6-well plates as described above. In order to minimize proliferation, cells were pre-starved

for 24h in Opti-MEM (Life technologies). Each condition was assessed in duplicates.

Scratching was performed in the middle of the well using a 10 µL pipette tip. Remaining cell

debris was washed away with pre-warmed PBS, before reduced culture medium was applied

(only containing 2% FCS). Images were taken immediately after scratching at time point t0 and

after 24 h as described in chapter 2.6.2 Wound healing assay images.

2.1.6 Trans-well migration assay

Trans-well assay, also called Boyden chamber assay, was performed with stable A549

miR-574-5p overexpression and control cells. Cultured cells were detached as described

above and sedimented for 5 min at 1,200 rpm (Eppendorf Centrifuge 5702). The cell pellet was

then resuspended in serum-free culture medium without any FCS. 5 x 104 cells were seeded

in a volume of 100 µL in a 24-well cell culture insert (Corning, Cat. No. 353097). Those inserts

have a membrane on the bottom with 8 µm pores to enable migration of A549 cells. The inserts

were placed in a 24-well plate and cells were allowed to adhere for 20 min at room temperature.

Subsequently, the bottom of the wells was filled with 700 µL of full complemented culture

medium to encourage migratory behavior (see Figure 10). As negative control, one well was

filled with serum-free medium which restrains migration.

Samples were cultured for 5 h at 37°C. Then, the inserts were washed in PBS. Non-migrated

cells that were still on the inside of the insert were removed by swiping. Migrated cells on the

bottom of the insert were fixed for 3 min at room temperature by placing the inserts in new

wells filled with 700 µL of methanol (VWR). Afterwards, cells were stained with 0.5% crystal

violet (Carl Roth) for 10 min at room temperature. Residual dye was remove by washing the

inserts with autoclaved Millipore water (MQ) followed by an additional swiping step to remove

cells from the inside of the insert. The membranes were then cut out using a scalpel and were

mounted on cover slides with Pertex® (VWR). Number of violet migrated cells was counted

under a light microscope using a manual cell counter. The protocol was kindly provided by Dr.

Kati Turkowski and PD Dr. Rajkumar Savai, Max-Planck-Institute for Heart and Lung

Research, Bad Nauheim.


34

Figure 10: Boyden chamber set-up.

Cells are seeded in a cell culture insert with a membranous bottom (pore size 8 µm) and migrate towards an increased FCS concentration. After 5 h, migrated cells on the bottom of the membrane can be fixed, stained and counted under a light microscope.

2.2 RNA methods

2.2.1 RNA extraction

Total RNA was extracted using TRIzol Reagent (Invitrogen) performing a standard phenol

chloroform protocol. In short, cell pellets were resuspended in an appropriate amount of TRIzol

and incubated for 5 min. Then, 200 µL Chloroform (Carl Roth) were added and samples were

thoroughly mixed before incubation on ice for 15 min. After centrifugation for 15 min at 4°C

with 17,000 x g, the upper aqueous phase containing the RNA was transferred to a fresh tube

and 500 µL isopropanol (VWR), 5 µL 3M sodium acetate (pH 6.5) (Carl Roth) and 1 µL

GlycoBlue™ (Thermo Fisher Scientific) were added and mixed. Samples were incubated

further 15 min on ice before another centrifugation step was performed. The precipitated RNA

pellet was then dissolved in an appropriate amount of MQ. The RNA was then treated with

Turbo DNase (Ambion) for 5 min according to the manufacturer’s instructions to remove

residual DNA. DNase digested RNA was incubated for 30 min at -80°C together with 100 µL

ethanol (VWR), 2 µL 3 M sodium acetate (pH 6.5) and 1 µL Glyco blue™. RNA was then

precipitated for 30 min at 17,000 x g at 4°C. The pellet was washed with 70% ice-cold ethanol

by rinsing the pellet and a subsequent centrifugation for 5 min at 17,000 x g at 4°C. The RNA

pellet was air dried for 5 min at room temperature and resuspended in an appropriate amount

of MQ. RNA concentration and purity were then measured by UV spectroscopic measurement

using the NanoDrop ND-1000 spectrophotometer (Thermo Fischer Scientific).

2.2.2 mRNA or miR quantification by qRT-PCR

For mRNA quantification, 1 µg of DNase-treated RNA was used for reverse transcription.

Therefore, the High-Capacity RNA-to-cDNA Kit (Applied Biosystems) was used according to

manufacturer's instructions. Generally, 10 µL 2x RT buffer and 1 µL RT enzyme were added

to a RNA mix in 9 µL. Reverse transcription was conducted in a thermo cycler (Bio-Rad

Laboratories) for 1 h at 37°C and 5 min at 95°C. Real-time PCR was then performed with the

StepOne Plus™ Real-Time PCR System (Applied Biosystem) using Power Syber Green PCR

Master Mix (Applied Biosystems). The PCR program can be found in Table 2. In general, one

reaction contained 10 µL 10x Sybr Green Mastermix, 3.75 µL forward Primer (2 µM diluted in


35

MQ), 3.75 µL reverse Primer (2 µM diluted in MQ), 1.5 µL MQ and 1 µL cDNA (1:2 diluted in

MQ). Each sample was set up in duplicates. To normalize variations in cDNA quantities

throughout the samples, β-Actin was used as housekeeping gene. Fold inductions were

calculated using the 2(-∆Ct) method. A list of primers can be found in Table 3.

Table 2. PCR program for mRNA quantification

Step Temperature Time Repeats

Initial denaturation 95°C 20 s x

Denaturation 95°C 3 s 40 cycles

Annealing & Elongation 60°C 30 s

95°C 15 s x

Melt curve 60°C 1 min x

95°C 15 s x

Table 3. Primer used for qRT-PCR

Target forward primer sequence reverse primer sequence

β-Actin CGGGACCTGACTGACTAC CTTCTCCTTAATGTCACGCACG

cJun TCG ACA TGG AGT CCC AGG A GGC GAT TCT CTC CAG CTT CC

SMAD2 GGGATGCTTCAGGTAGGACA TCTCTTTGCCAGGAATGCTT

SMAD3 CGCAGAACGTCAACACCAAG GGCGGCAGTAGATGACATGA

COX-2 CCGGGTACAATCGCACTTAT GGCGCTCAGCCATACAG

NDUFS2 GTTTTGCCCATCTGGCTGGT CATGCCATGGCCTATGGTGAA

mPGES-1 GAAGAAGGCCTTTGCCAAC CCAGGAAAAGGAAGGGGTAG

UBE2R2 ATGTGGCACCCCAACATT TCCACCTTTCAGAAGGCAGT

CEP41 ACAGAACCCAAGATACCAGCATAT GGGAGCTGGTAAGATACACACA

SLC39A6 GCACTTACTGCTGGCTTATTCA CGGCTACATCCATGGTCACT

PAIP2 CCATTTGCAGAGTACATGTGGA CCGTACTTCACCCCAGGAAC

GTF2E2 CCATGCAGGAATCTGGACCA AATCCTTCAGCACTCCAGCC

LEO1 ACTGCCCAACTTTCTCAGTGT AGATGATTGTGGTCGCCCTG

For miR quantification, the Qiagen miScript system was used according to manufacturer’s

instructions. One reverse transcription reaction comprised of 4 µL 5x miScript HiSpec Buffer,

2 µL 10x miScript Nucleic Acid Mix, 2 µL reverse transcriptase and 1 µg DNase digested RNA

in MQ ad 20 µL. The reverse transcription was performed for 1 h at 37°C and 5 min at 95°C

using the thermo cycler (Peqlab Biotechnologie GmbH, advanced primus 25). CDNA was

diluted 1:2 in MQ afterwards. Following, real time PCR was performed using either the miR-

574-5p specific primer (MS00043617, Qiagen) or miR-16-5p (MS0031493, Qiagen). Real-time

PCR was performed according to the manufacturer’s instructions (see Table 4). Generally, a

mix for one reaction contained 12.5 µL QuantiTect Syber Green Mastermix, 2.5 µL 10x miScript

Universal primer, 2.5 µL specific primer, 1 µL miScript cDNA and 6.5 µL MQ. Fold inductions

were calculated as described above.


36

Table 4. PCR program for miR quantification

Step Temperature Time Repeats

Initial denaturation 94°C 15 s x

Denaturation 94°C 15 s

40 cycles Annealing 55°C 30 s

Elongation 70°C 30 s

94°C 15 s x

Melt curve 60°C 1 min x

95°C 15 s x

2.2.3 RNA immunoprecipitation (RIP)

The RIP protocol was kindly provided by Prof. Michaela Müller-McNicoll, Goethe University

Frankfurt. In order to precipitate CUGBP1, the GammaBind Plus Sepharose beads (GE

Healthcare) were used. In preparation, the beads were blocked with blocking buffer containing

0.2 mg/mL bovine serum albumine (BSA) in PBS (Sigma-Aldrich) and 0.1 mg/mL yeast tRNA

for 90 min at 4°C. Then, beads were washed with PBS and stored at 4°C until further use.

A549 cells were seeded at a density of 3 x 106 in a 10-cm dish and incubated overnight. In

case of stimulation, 5 ng/mL IL-1β were added for further 24 h. In order to harvest the cells,

they were washed with 5 mL of ice-cold PBS and scraped in 5 mL PBS complemented with

protease inhibitor EDTA-free (Roche). Cells were spinned down for 5 min and 400 x g at 4°C

and resuspended in 1 mL lysis buffer (see Table 5). The suspension was incubated 10 min on

ice and then sonicated 4 times for 10 seconds on 30% amplitude, with 20 sec pause (Branson

Sonifier 250). Then, cell debris was spinned down for 10 min at 10,000 x g at 4°C. The

supernatant was transferred into a fresh tube and 10% were taken as input sample.

Before usage, the blocked beads were washed 3 times with lysis buffer and were centrifuged

at 300 x g for 5 min. Beads and antibodies were linked by mixing 50 µL bead suspension with

10 µg of CUGBP1 antibody (05-621 clone3B1, Merck) or normal mouse IgG antibody (12-371,

Merck) followed by incubation for 30-60 min at 4°C. Afterwards, immunoprecipitation (IP) was

conducted by dividing the lysate equally to the CUGBP1- /IgG-bead mixture and incubating for

2 h at 4°C. Then, samples were washed with each wash buffer B1-B3 (composition see Table

5) for 5 min in the cold room, with centrifugation steps of 5 min and 300 x g in between. After

the last washing step, 10% of each precipitate was taken for Western blot analysis in order to

validate the immunoprecipitation (see chapter 2.3.3 SDS-PAGE and Western Blot). The

remaining precipitates were resuspended in 500 µL TRIzol reagent (Invitrogen) and RNA was

isolated as described above (see chapter 2.2.1 RNA extraction). Thereby, it was important to

take the exact same volume of aqueous phase from each sample, since the complete isolated

RNA was then used for reverse transcription.


37

Since there was no housekeeping gene for analysis of the real time PCR, data analysis was

conducted without building a difference to a Ct of a housekeeping gene. After calculating the

2(-Ct), the Input value was multiplied by 10 to even out that it was 10% of the total cell lysate,

while IgG-IP and CUGBP1-IP values were multiplied by 1.11 since 10% were taken for

Western blot analysis and 90% were left. Then, a x-fold was calculated to obtain the RNA

enrichment and the yield. The yield describes the amount of precipitated RNA in percent

compared to the input, while the enrichment shows the specificity of CUGBP1-IP in comparison

to IgG-IP. In this thesis, the graphs will depict the enrichment, as it gives the information if a

certain RNA is indeed bound by CUGBP1.

𝑦𝑖𝑒𝑙𝑑 =2−𝐶𝑇 𝑜𝑓 𝐼𝑃 × 1.11

2−𝐶𝑇 𝑜𝑓 10%𝐼𝑛𝑝𝑢𝑡 × 10

𝑒𝑛𝑟𝑖𝑐ℎ𝑚𝑒𝑛𝑡 =2−𝐶𝑡 𝑜𝑓 𝐶𝑈𝐺𝐵𝑃1 × 1.11

2−𝐶𝑇 𝑜𝑓 𝐼𝑔𝐺 × 1.11

Table 5. RIP buffer composition

Buffer End concentration Reagent

Hypotonic lysis buffer

10 mM Tris-HCl pH 7.5 (Sigma-Aldrich)

10 mM KCl (Sigma-Aldrich)

1.5 mM MgCl2 (Sigma-Aldrich)

0.5 mM Ll fac (Carl Roth)

0.9% NP-40 (Igepal) (Sigma-Aldrich)

1x Protease inhibitor cocktail (Roche)

40 U/µL Ribonuclease Inhibitor

Ad 10 mL MQ Wash buffer B1 20 mM Tris-HCl pH 7.5 (Sigma-Aldrich)

150 mM NaCl (Sigma-Aldrich)

2 mM EDTA (Roche)

0.1% SDS (Carl Roth)

1% Triton X-100 (Carl Roth)


Ad 10 mL MQ

Wash buffer B2 20 mM Tris-HCl pH 7.5 (Sigma-Aldrich)

500 mM NaCl (Sigma-Aldrich)

2 mM EDTA (Roche)

0.1% SDS (Carl Roth)

1% Triton X-100 (Carl Roth)


Ad 10 mL MQ Wash buffer B3 10 mM Tris-HCl pH 7.5 (Sigma-Aldrich)

250 mM LiCl (Sigma-Aldrich)

1 mM EDTA (Roche)

1% Na Deoxycholate (Sigma-Aldrich)

1% NP-40 (Igepal) (Sigma-Aldrich)


Ad 10 mL MQ


38

2.3 Protein methods

2.3.1 Soluble and microsomal fraction preparation

As described in [250] [195], cell pellets of A549 cells were resuspended in 1 mL

homogenization buffer (0.1 M potassium phosphate (Carl Roth) pH 7.4, 0.25 M sucrose

(Scharlau), and EDTA-free protease inhibitor (Roche)). The suspension was then sonicated 4

x 10 seconds on ice with 20 seconds pause (Branson Sonifier 250). To remove all debris,

samples were centrifuged with 5,000 x g at 4°C for 10 min and the supernatant was transferred

in ultracentrifugation tubes. Then, samples were centrifuged at 100,000 x g at 4°C for 1 h

(using Beckmann Optima XL-100K). The resulting supernatants contained the soluble fractions

and were stored at -80°C while, the pellets were mixed with 500 µL 2.5 M NaBr (Sigma-Aldrich)

and incubated for 45 min on ice with shaking. Another centrifugation step was performed at

4°C for 1 h at 100 000 x g. The supernatant containing membrane associated fraction was

discarded, while the pellets resembled the microsomal protein fraction. They were

resuspended in PBS supplemented with EDTA-free protease inhibitor (Roche) and sonicated

on ice to improve dissolving of the proteins. All samples were stored at -80°C until further use.

Concentration for Western blot analysis was measured using Bradford assay (Bio-Rad

Laboratories) (see next chapter) while for proteomics samples the protein amount was

determined by Pierce BCA Protein Assay (Thermo Fisher Scientific) following manufacturing

instructions.

2.3.2 Determination of protein concentration

Concentration of the protein solution was determined by Bradford assay (Bio-Rad

Laboratories) according to manufacturer's instructions. A standard curve with bovine serum

albumin (BSA) concentrations of 50-500 µg/mL was used. 10 μL of the different BSA solutions

or protein samples (diluted 1:20 in MQ) were mixed in a 96-well plate and 190 μL of Bradford

reagent (diluted 1:5 in MQ) was added. Each sample and standards were measured in

duplicates. The absorption at a wavelength of 595 nm was measured at a Tecan Infinte M 200

(Tecan Group). The protein concentration was calculated according to the standard curve.

2.3.3 SDS-PAGE and Western Blot

Proteins were separated by Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-

PAGE). Depending on the approach, either 80 µg of soluble/microsomal proteins or for RIP

samples 20 µL were mixed with 5 µL 4x protein loading buffer containing 50% glycerine, 1.5%

bromphenol blue, 4% SDS, 15% β-Mercaptoethanol (all Carl Roth). Samples were then boiled

for 5 min at 95°C in order to denature proteins and loaded on a 12% SDS-Gel (composition

see Table 6). As a marker the Precision Plus Protein™ All Blue Standard (Bio-Rad


39

Laboratories) was also applied and the gel ran for ca. 1 h at 120 V. Afterwards, proteins were

transferred to a HyBond ECL nitrocellulose membrane (Amersham) with 230 mA for 80 min.

Subsequently, membranes were blocked with Odyssey blocking buffer (LI-COR® Bioscience)

for 1 h at room temperature. Then, they were incubated over night at 4°C with primary

antibodies (see Table 7). The next day, membranes were washed 3 times with PBS pH 7.4

complemented with Tween20 0.1% (v/v) (Carl Roth) (PBS-T) to remove residual unbound

antibody. Then an infrared dye conjugated secondary antibody (IRDye®, LI-COR®

Bioscience) directed against the certain host animal, was incubated on the membranes for

45 min at room temperature. Membranes were washed three times with PBS-T before

visualization. Detection and quantification were performed using the Odyssey Infrared Imaging

System (LI-COR® Biosciences) and the Image Studio Software.

Table 6. Gel composition for SDS-PAGE

Stacking gel (7.4 %) Separating gel (12%)

MQ 1.2 mL MQ 1.6 mL

Acrylamide 30% (w/v) 266 µL Acrylamide 30% (w/v) 2 mL

Tris-HCl, 0.5 M, pH 6.8 500 µL Tris-HCl, 1.5 M, pH 8.8 1.3 mL

SDS 10% (w/v) 20 µL SDS 10% (w/v) 50 µL

APS 10% (w/v) 12 µL APS 10% (w/v) 50 µL

TEMED 3 µL TEMED 4 µL

Table 7. Primary antibodies for Western blot analysis

Target Host Supplier (order no.)

β-Actin goat Santa cruz (sc-1616)

CUGBP1 mouse Abcam (ab9549)

mPGES-1 rabbit Cayman (cay160140)

NDUFS2 rabbit Abcam (ab96160)

SMAD2 goat Santa cruz (sc-6200)

SMAD3 rabbit Abcam (ab28379)

SMAD4 mouse Abcam (ab3219)

P38 goat Santa cruz (sc-535-g)

CUGBP1 (RIP-Western blot) rabbit Abcam (ab129115)

2.3.4 TMT labelling and mass spectrometry

Soluble or microsomal proteins (see chapter 2.3.1 Soluble and microsomal fraction

preparation) were solubilized in 50 μl buffer containing 0.05 M triethylammonium bicarbonate,

4 M Urea, 0.01 % SDS and 1 mg RapiGest SF Surfactant (Waters). From each sample 50 µg

were taken for further preparation. Disulfide reduction was conducted for 30 min at 56°C by

adding 5 μl 1 M DTT. Afterwards, sulfhydryl alkylation was performed by adding 4 μl 1 M

iodoacetamide solution, while samples were incubated at room temperature for 1 h in the dark.


40

Trypsin (modified sequencing grade, Promega) was added in a ratio of 1:30 (trypsin: protein).

Then, samples were incubated at 37°C overnight. By using tandem mass tags (TMT 6-plex)

according to manufacturer's instructions (ThermoFisher Scientific), peptides were labelled.

Using an SCX-cartridge (Phenomenex), excess reagents could be removed from the samples.

Liquid chromatography tandem mass spectrometry (MS) of a TMT-labeled sample was

performed on QExactive mass spectrometer (ThermoFisher Scientific).

Peptide pre-fractionation was conducted as previously described in [251]. TMT-labeled protein

samples were separated over a 60-minute gradient (3-55 % B) on a 2.1 × 250 mm XBridge

BEH300 C18 column (Waters) using a flow rate of 200 µL/min. Buffers A contained 20 mM

ammonia in MQ, whereas buffer B contained 20 mM ammonia in 80 % acetonitrile. The specific

fractions were collected every minute and the fractions covering the peptide elution range were

concatenated to yield 12 final pooled fractions. These fractions were evaporated to dryness by

vacuum drying and stored at -20°C until nano Liquid chromatography-mass spectrometry (LC-

MS) data capture. A Q-Exactive mass spectrometer (Thermo Scientific) was used to performe

online LC-MS measurements. Peptide samples were trapped on an Acclaim PepMap trap

column (C18, 3 µm, 100Å, 75 µm x 20 mm). Separation took place on a 15-cm long C18 picofrit

column with 100 μm internal diameter and 5 μm bead size (Nikkyo Technos) which was

installed onto a nano-electrospray ionization source. Solvent A was 97% water, 3%

acetonitrile, 0.1% formic acid; and solvent B was 5% water, 95% acetonitrile, 0.1% formic acid.

At a constant flow of 0.25 μl/min, the curved gradient went from 3% solvent B up to 48% solvent

B in 50 min. Fourier transform mass analyzers (FTMS) master scans with 70,000 resolutions

(and mass range 400-1200 m/z) were followed by data-dependent MS/MS (17,500 resolution)

on the top 10 ions using higher energy collision dissociation (HCD) at 31% normalized collision

energy. Precursors were isolated with a 2 m/z window. Automatic gain control (AGC) targets

were 3e6 for MS1 and 2e5 for MS2. Maximum injection times were 250 ms for MS1 and 200

ms for MS2. Dynamic exclusion was used with 20 s duration. Precursors were excluded when

they showed unassigned charge state or charge state of 1. An underfill ratio of 1% was used.

2.4. Bioinformatical methods

2.4.1 3’UTR analysis

For the analysis of splice patterns, proteins that were upregulated at least 1.5-fold in response

to ΔCUGBP1 in the mass spectrometry dataset were taken into account. This list of 399

proteins was then analyzed concerning their 3’UTRs. All described 3’UTR isoform sequences

were downloaded from ensemble biomart (Human, GRCh38.p12 Ensembl variation resources

[252]), resulting in a list of 1916 transcripts. Those sequences were then aligned with 42 known


41

binding motifs of CUGBP1 downloaded from the online tool Splice Aid F [109] [104]. Alignment

was kindly conducted by Tobias Saul.

For the high stringency analysis, three criteria were applied: (I) the binding sites should be of

39 or 46 nt length, (II) there should at least be 2 binding sites and (III) those binding sites

should span a potential intron of at least 1000 nt. For a second less stringent approach

(referred to as low stringency analysis), the three criteria were mitigated: (I) the binding sites

should at least be of 8 nt length, (II) there should be 2 or more binding sites and (III) those

binding sites should span a potential intron of minimum 100 nt. Analysis was performed with

Microsoft Excel 2016.

2.4.2 Mass Spectrometry data analysis

Acquired MS raw files were searched using Sequest-Percolator under the software platform

Proteome Discoverer 1.4.1.14 (Thermo Fisher Scientific) against human Uniprot database

(release 01.12.2015 [89]) and filtered to a 1% false discovery rate (FDR) cut off. A precursor

ion mass tolerance of 10 ppm was used as well as product ion mass tolerances of 0.02 Da for

HCD-FTMS and 0.8 Da for collision induced dissociation Ion Trap Mobility Spectroscopy (CID-

ITMS). The algorithm considered tryptic peptides with maximum 2 missed cleavages;

carbamidomethylation (C), TMT 6-plex (K, N-term) as fixed modifications and oxidation (M) as

dynamic modifications. Quantification of reporter ions was done by Proteome Discoverer on

HCD-FTMS tandem mass spectra using an integration window tolerance of 10 ppm. Only

unique peptides in the data set were used for quantification. Fold values were calculated

comparing proteins from ΔCUGBP1 to Scramble, ΔmiR-574-5p to negative control LNA and

miR-574-5p oe to negative control mimic. Fold values of +1.5/-1.5 were considered up- or

downregulated.

2.4.3 Ingenuity pathway analysis (IPA)

Most genes are regulated by a variety of upstream regulators or transcription factors with often

opposing effects. As it is unknown which will dominate in this specific system, predictions

become difficult. Therefore, a statistical approach was used. A quantity “z‐score” was

calculated that rates whether an upstream regulator has significantly more “activated”

predictions than “inhibited” predictions (z > 0). The other way around, when there are more

inhibited predictions than activating predictions, the z-score will be negative. All regulated

proteins in soluble as well as microsomal fractions from all three conditions were analyzed

using IPA (Ingenuity Systems, www.ingenuity.com). The 5 most affected canonical pathways

were predicted. The canonical pathways with p-values ≤0.05 were defined as significant.


42

2.5 Fluorescent labeling techniques

To find if the two binding partners CUGBP1 and miR-574-5p co-localize within the cell,

immunostaining as well as in situ hybridization techniques were performed by Julia Wellstein.

2.5.1 Immunostaining

Immunofluorescent stainings were performed to visualize CUGBP1. Therefore, A549 cells

were seeded at a density of 2.5 x 105 per well in a 6-well plate on glass cover slips (12 mm,

Neolab) and cultured overnight. Medium was removed and cells were washed with PBS, before

fixation for 10 min with 4% formaldehyde (FA, Carl Roth). After 3 PBS washing steps for 3 min

to remove residual FA, cells were permeabilized for 10 min with 0.5% Triton X-100 (Sigma-

Aldrich) diluted in PBS. Subsequently, cells were blocked for 20 min with a 2% BSA solution

(Sigma-Aldrich) in PBS. The CUGBP1 primary antibody (ab9549, Abcam) was diluted 1:500

in blocking solution applied on the cells and incubated for 1h at room temperature. Following,

3 washing steps à 5 min with 0.01% Tween20 (Carl Roth) in PBS were performed. Then, cells

were incubated for 45 min at room temperature with the secondary antibody goat anti-mouse

IgG (Alexa Fluor® 594, ab150116, Abcam) diluted 1:500in blocking solution. Finally, cells were

washed with 0.01% Tween20 (Carl Roth) in PBS, counterstained for 5 min with 4′,6-diamidine-

2′-phenylindole dihydrochloride (DAPI 1µg/ mL in PBS), Sigma-Aldrich) and mounted in Mowiol

4-88 mounting medium (Sigma-Aldrich).

2.5.2 Fluorescence in situ hybridization (FISH)

To visualize miR-574-5p within the cells, FISH was performed with A549. To that end, 2.5x105

A549 cells were seeded on glass cover slips (12 mm, Neolab) in 6-well plates and cultured for

24 h. Cells were washed with PBS and prefixed with 1% FA in PBS for 10 min. After 3 washing

steps of 3 min with PBS, cells were permeabilized with 0.5% Triton X-100 in PBS for 20 min

on ice. After additional 3 PBS washing steps of 3 min, cells were refixed for 10 min with 4%

FA. Then, 3 further washing steps with PBS were followed by prehybridization for 30 min at 40

°C in microRNA ISH buffer (Qiagen). Then, hybridization took place at 54°C with 100 nM

double digoxigenin (DIG) labeled miR-574-5p probe (Qiagen) diluted to final concentration of

100 nM in microRNA ISH buffer for 1 h. Afterwards, cells were washed twice for 5 min with 2x

saline-sodium citrate buffer (Gibco) at 54°C and once at room temperature. Blocking was then

performed with 2% BSA in PBS for 20 min. The rabbit anti-DIG antibody (9H27L19, Invitrogen,

Thermo Fisher Scientific) was diluted 1:40 in blocking solution and cultured on the cells at

room temperature for 1 h. Residual antibody was removed by washing with 0.01% Tween20

in PBS 3 times for 5 min. Secondary antibody goat anti-rabbit IgG (Alexa Fluor® 594, 111-585-

144, Jackson ImmunoResearch) diluted 1:300 in blocking solution was incubated on the cells


43

for 45 min at room temperature. Finally, cells were washed with 0.01% Tween20 in PBS,

counterstained with DAPI and mounted as described above.

2.6 Microscopy and image acquisition

2.6.1 Immunostaining and FISH images

In order to take confocal images of immunofluorescence and FISH samples, the Leica TCS

SPE confocal point scanner mounted on a Leica DMi8 stand equipped with an oil immersion

63x Apochromat was used. In all cases, the 405 and 561 nm laser lines were used to perform

excitation. Images were analyzed using the ImageJ software (http://imagej.nih.gov/ij/) and

show one focal plane of the middle of the nucleus.

2.6.2 Wound healing assay images

Images were acquired with 10 x magnitude using Axiovert 200 (Zeiss) with the LSM 510 Meta

(Zeiss) image-processing software. One image was taken per well at the exact same

localization immediately after scratching (t0) and 24 h later. Analysis of the images was

performed using ImageJ software (http://imagej.nih.gov/ij/). Therefore, the width of the scratch

was measured at 3 random sites within each picture. Since there were two wells per condition,

this resulted in 6 values per time point which were used to build a mean value.

2.7 Statistics

Results are given as mean +SEM of at least three independent experiments. Statistical

analysis was carried out by Student's paired or unpaired t-test (two-tailed), respectively, using

GraphPad Prism 5.0. Differences were considered as statistically significant for p≤0.05

(indicated as * for p≤0.05, ** for p≤0.01 and ***for p≤0.001).

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3. Results

In the center of this thesis is the non-canonical decoy mechanism of CUGBP1 binding to miR-

574-5p. This mechanism was recently described to regulate the expression of the CUGBP1

target mPGES-1 [196]. Therefore, the first steps were to proof the interaction of CUGBP1 with

the mPGES-1 mRNA and with miR-574-5p.

3.1 Verification of CUGBP1 binding via RIP

In order to proof the interaction of CUGBP1 with mPGES-1 and especially miR-574-5p, RIP

assays were conducted. Thereby, CUGBP1 was precipitated from a whole cell lysate of

unstimulated or IL-1β-stimulated A549 cells with a specific antibody that was coupled to

sepharose beads. As mock control, a normal mouse IgG antibody was used for precipitation.

CUGBP1 protein as well as the RNAs bound by CUGBP1 could then be quantified via Western

blot and qRT-PCR, respectively. Binding was assumed, if the specific mRNA showed an

enrichment in CUGBP1-IP, compared to IgG-IP.

3.1.1 Establishment of CUGBP1 RIP protocol

In a first validation step, the precipitation of CUGBP1 had to be confirmed via Western blot

analysis. Therefore, 10% of the input, as well as IgG- and CUGBP1-IP samples were applied

on a SDS-Gel. The Western blot membrane was then incubated with a CUGBP1 antibody from

another host animal than the RIP antibody. Otherwise the images were not evaluable, because

the sepharose beads could not be removed from the samples and create a smear. The

Western blot images demonstrated that a clear CUGBP1 band was detected in the CUGBP1-

IP sample at 48 kDa but not in the IgG sample (see Figure 11A). Hence, precipitation was

indeed successful. Naturally, the CUGBP1 band with the highest intensity was detected in the

Input sample. The amount of precipitated CUGBP1 was calculated based on the input,

revealing that 33.2% (±5.5 SEM) of total CUGBP1 were recovered.

Following, verification of RNA co-precipitation was necessary. Therefore, the first described

CUGBP1 target cJUN was measured performing qRT-PCR [253]. Indeed, qRT-PCR results

revealed a significant 2.4-fold enrichment compared to IgG-IP (see Figure 11B). Together with

the Western blot analyses, this ensured the accuracy of the RIP experiments, allowing for

quantification of other potential CUGBP1 targets in following experiments.

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Figure 11: Validation of RIP protocol.

RIP of A549 cells with antibodies against CUGBP1 or normal mouse IgG. (A) Precipitated CUGBP1 protein was analyzed via Western blot analysis. Quantification was performed with Odyssey Infrared Imaging System (LI-COR® Biosciences). Input was set as 100%. Percentage of precipitated CUGBP1 is given as mean +SEM. One representative image of n=4 independent experiments is shown. (B) Co-precipitated cJUN mRNA was quantified using qRT-PCR. Relative enrichment normalized to IgG is given as mean +SEM of n=4 experiments., t-test, **p ≤ 0.01, ****p ≤ 0.0001

3.1.2 CUGBP1 binds to miR-574-5p and mPGES-1 mRNA

Previous work from our lab provided first evidence that the RBP CUGBP1 can bind miR-574-

5p under inflammatory conditions, which prevents it from binding to its actual target, the

mPGES-1 mRNA [196]. Within this thesis, the binding of CUGBP1 to both mPGES-1 and miR-

574-5p needed to be confirmed. RIP of unstimulated A549 cells was conducted and in fact,

mPGES-1 mRNA was significantly enriched in CUGBP1-IP in comparison to IgG-IP samples

(see Figure 12A). As negative control, COX-2 was tested as it does not have any binding sites

for CUGBP1 [196] and therefore was not enriched in the CUGBP1-IP samples. These findings

further confirmed the interaction of CUGBP1 with mPGES-1 mRNA.

For miRs, IL-1β-stimulated A549 cells were used to initiate the decoy. MiR-16-5p served as

negative control and was certainly not bound by CUGBP1. MiR-574-5p however, depicts a

sequence homology to CUGBP1 binding motifs [196]. In fact, miR-574-5p was strongly

enriched 22-fold in the CUGBP1-IP samples which proves the interaction (see Figure 12B).

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Figure 12: CUGBP1 binding to mPGES-1 mRNA and miR-574-5p in RIP assays.

RIP of A549 cells with antibodies against CUGBP1 or normal mouse IgG. Co-precipitated RNAs were quantified using qRT-PCR. (A) mRNA quantification of mPGES-1 and COX-2 in unstimulated A549 cell IP samples. (B) miR quantification of miR-574-5p and miR-16-5p in IP samples of A549 cells stimulated with IL-1β for 24 h. Relative enrichment normalized to IgG is given as mean +SEM of n=3 (A) or n=7 (B) experiments, t-test, *p ≤ 0.05.

These results further confirmed the existence of a decoy mechanism of miR-574-5p and

CUGBP1. Thus, it was intriguing to further characterize this novel miR function and investigate

if it influences the proteome of A549 cells in a broader way.

3.2. TMT-based proteomics study of IL-1β-stimulated A549 cells

A549 cells were cultured by Dr. Isabel Baumann, stimulated with 5 ng/mL IL-1β and transfected

with siRNA, LNA or mimics, respectively. Cells with ΔCUGBP1, ΔmiR-574-5p or miR-574-5p

oe were compared to their corresponding controls. Knockdown and overexpression of miR-

574-5p were previously established by Dr. Isabell Baumann and revealed a significant ~ 80%

decrease of miR-574-5p, while miR-574-5p was ~ 300-fold upregulated as quantified via qRT-

PCR as stated in [196]. Knockdown of CUGBP1 was validated via Western blot analysis and

showed a reduction of 67% in the soluble fraction and 83% in the microsomal fraction of A549

cells (see Figure 13).

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Figure 13: Quantification of CUGBP1 knockdown. siRNA-mediated knockdown of CUGBP1 in A549 cells was followed by 24 h stimulation with IL-1β. Fractionation resulted in a soluble and a microsomal protein fraction which were analyzed via Western blot. One representative blot is shown from n=3 independent experiments. β-Actin was used as loading control. Quantification was performed with Odyssey Infrared Imaging System (LI-COR® Biosciences). Fold inductions compared to control are given as mean +SEM. t-test, **p ≤ 0.01. In order to identify gene expression changes, A549 cells stimulated with IL-1β were used for a

TMT-based LC/MS-MS study which was conducted by the group of Per-Johan Jakobsson, KI,

Sweden (see Figure 14). Cell lysates were fractionated to separate soluble and microsomal

proteins. All LC-MS/MS samples were then digested and labeled with TMT 6-plex to allow

quantitative protein comparison. This technique facilitates a sensitive multiplex analysis and

controls for technical variations [254]. TMT quantification is conducted by analyzing the

intensities of fragment reporter ions released from the labels in the tandem MS mode (MS2)

during peptide fragmentation. In the full scan mode (MS1) to be fragmented, precursor ions

were selected.

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Figure 14: TMT based proteomics approach.

A549 cells were transfected creating ΔCUGBP1, ΔmiR-574-5p or miR-574-5p oe as well as respective controls and stimulated with IL-1β for 24 h. Fractionation of the cell lysates resulted in soluble and microsomal protein fractions which were separately analyzed. All samples were trypsin digested, labelled with Tandem mass tags (TMT) and analyzed by LC-MS/MS. Data were analyzed concerning fold-changes of protein levels compared to each control.

3.2.1 Proteome changes in A549 upon ΔCUGBP1, ΔmiR-574-5p and miR-574-5p oe

In the soluble fraction a total amount of 2441 proteins was detected in the ΔCUGBP1 samples,

while in the ΔmiR-574-5p cells 2449 and in miR-574-5p oe samples a total number of 2450

proteins were measured. Whereas in the microsomal fraction a significantly higher number of

proteins was detected: 3970 proteins in ΔCUGBP1 samples, 3967 proteins in ΔmiR-574-5p

and 3968 proteins in miR-574-5p oe samples. Accordingly, roughly the same numbers of total

proteins were identified throughout the three conditions. Besides the high numbers of total

proteins, only small percentages of them exhibited up- or downregulation in comparison to the

corresponding control samples (see Figure 16). In general, a protein was considered as

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upregulated starting from a TMT ratio of ≥ 1.5 (fold change ≥ 1.5) and as downregulated with

a TMT ratio of ≤ 0.5 (fold change ≤ -1.5).

Canonical CUGBP1 targets, as well as decoy targets, as a subpopulation of those, were

supposed to show an upregulation in response to the knockdown (see Figure 15), while

downregulation potentially indicated a secondary effect. In fact, 2% of the proteins detected in

the soluble fraction were upregulated (61 proteins), while 8% (187 proteins) showed a

decreased expression level (see Table 8). In the microsomal fraction, more proteins were up-

than downregulated: 9% (338 proteins) exhibited an increased protein level, whereas only 4%

(152 proteins) showed a reduced one. As described, decoy targets could not certainly be

identified in the ΔCUGBP1 samples as they are thought to show the same regulation like

canonical CUGBP1 targets. Thus, the two miR conditions were more relevant in that regard.

Figure 15: Expected regulations in the proteomics study. Discrimination of canonical CUGBP1/ miR-574-5p and decoy targets via expected regulation on protein level. ↑ indicates increased protein level, ↓ indicates decreased protein level.

In miR-574-5p oe samples, decoy targets were expected to be increased in response to high

miR-574-5p levels, whereas reduced protein levels would refer to canonical miR targets (see

Figure 15). Vice versa, the opposite could be assumed for the ΔmiR-574-5p condition. In the

soluble fraction of miR-574-5p oe samples, 2% (40 proteins) were up- and 3% (78 proteins)

were downregulated. While 8% of microsomal proteins (303 proteins) were decreased upon

the overexpression, only 3% of protein levels were elevated (124 proteins) (see Table 8).

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In the soluble fraction, 1% (29 proteins) of the total protein amount was increased upon ΔmiR-

574-5p, while 14 proteins showed a decrease. However, the microsomal proteins showed

higher percentages, 9% were upregulated (345 proteins), while even 13% (504 proteins)

showed a downregulation (see Table 8).

Table 8. Numbers of increased (↑) and decreased (↓) proteins in each fraction and each condition of the proteomics study compared to their respective controls.

Soluble fraction Microsomal fraction

ΔCUGBP1 ↑ 61 338

↓ 187 152

miR-574-5p oe ↑ 40 124

↓ 78 303

ΔmiR-574-5p ↑ 29 345

↓ 14 504

Proteins which showed increased levels upon ΔCUGBP1 were analyzed for CUGBP1 binding

sites in their 3’UTRs. Sequences of the 3´UTRs of the specific proteins were downloaded from

the database “UCSC Genome Browser” (Dec. 2013 GRCh38/hg38 https://genome.ucsc.edu/)

[255]. Followed by identification of potential CUGBP1 binding motifs with the online tool

“SpliceAid 2” (www.introni.it/spliceaid.html [256]). The algorithm recognizes GREs and other

binding motives (e.g. CUGUCUG) in the provided 3’UTR sequences.

It was demonstrated that an excessive amount of the upregulated proteins does have a

potential CUGBP1 binding site: in the soluble fraction 69% (42 proteins) and in the microsomal

fraction 73% (246 proteins) of all detected proteins in the proteomics study (see Figure 16).

This indicates that many primary effects were observed herein. Preliminary analysis of

canonical miR-574-5p targets was accomplished by the microRNA.org. online tool [257].

Proteins that were upregulated upon ΔmiR-574-5p or downregulated upon miR-574-5p oe

were analyzed for miR-574-5p seed regions in their 3’UTRs. On average, only 16% of them

have a seed region, which strongly implies that there are many secondary effects observed in

this study (see Figure 16).

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Figure 16: Numbers of proteins differentially expressed upon ΔCUGBP1, ΔmiR-574-5p or miR-574-5p oe in soluble and microsomal fraction of the proteomics study. Cut-offs were set to 1.5-fold for upregulation (grey) and -1.5-fold for downregulation (black). CUGBP1 binding sites of upregulated proteins upon ΔCUGBP1 were predicted using splice aid [256]. MiR-574-5p seed regions were identified with the online tool microrna.org [257] of proteins revealing an upregulation upon ΔmiR-574-5p or a downregulation upon miR-574-5p oe. Table 9 presents a list of the overlap of potential miR-574-5p targets from soluble and

microsomal fraction as well as from the ΔmiR-574-5p and miR-574-5p oe samples. However,

as the focus of this project was clearly on canonical CUGBP1 and decoy targets, this approach

was not continued or further experimentally examined.

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Table 9. Potential canonical miR-574-5p targets.

Fraction Description Gene name

Microsomal Isoform 2 of Protein transport protein Sec24B SEC24B

Microsomal Mitochondrial inner membrane protein OXA1L OXA1L

Microsomal Isoform 2 of Methionine synthase MTR

Microsomal Isoform 2 of Phosphatidylinositol 5-phosphate 4-kinase type-2 gamma

PIP4K2C

Microsomal Sterol O-acyltransferase 1 SOAT1

Microsomal Transportin-3 TNPO3

Microsomal Transmembrane protein 33 TMEM33 Overlap analysis of proteins from the proteomics study that were upregulated upon ΔmiR-574-5p (65

proteins) and downregulated upon miR-574-5p oe (59 proteins) and which have a seed region in their

3’UTR. From the microsomal fraction seven proteins were left, while no proteins in the soluble fraction

fulfilled these criteria.

To summarize, these results demonstrate that on average 11% of all analyzed proteins could

be somehow regulated by CUGBP1, whereas 9.7% of all detected proteins depict a regulation

related to miR-574-5p. However, the distribution of the miR-574-5p regulated proteins varies

considerably depending on the protein fraction.

3.2.2 Validation of TMT proteomics study using Western blot analysis

For validation, the mass spectrometry data needed to be confirmed via Western blot analysis.

Distinct proteins were selected and it was examined whether they show the same regulation

upon ΔCUGBP1, ΔmiR-574-5p and miR-574-5p oe in Western blot data than in the proteomics

data. The proteomics study was a highly complex study with different conditions and fractions

(see Figure 14), which is why validation is not necessarily based on physiologically connected

proteins.

The first protein to be analyzed was NADH-Ubiquinone Oxidoreductase Core Subunit S2

(NDUFS2) which is a postulated CUGBP1 target in HeLa cells [84]. In the soluble fraction of

the proteomics study, NDUFS2 showed a trend towards a slight upregulation in response to

ΔCUGBP1 (1.3-fold) and miR-574-5p oe (1.4-fold). Western blot data proofed this regulation,

revealing a fold change of 1.6-fold upon miR-574-5p oe and even 2.2-fold in response to

ΔCUGBP1 (see Figure 17A, C). In ΔmiR-574-5p samples, NDUFS2 protein level seemed to

be slightly decreased (0.8-fold) in the proteomics data. This could also be confirmed using

Western blot analysis, where NDUFS2 was significantly reduced 0.8-fold in ΔmiR-574-5p

samples as well (see Figure 17B). In the microsomal fraction, NDUFS2 revealed no strong

changes in expression. For example, in response to ΔmiR-574-5p it showed a fold change of

1.1 in the proteomics data. This invariable protein level could also be demonstrated using

Western blot analysis, revealing a fold change of 1.1 (see Figure 17D).

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Apart from that, SMAD3 (Mothers against decapentaplegic homolog 3) was another protein

which was investigated. It was clearly downregulated (0.5-fold) in response to ΔCUGBP1 in

the soluble fraction of the proteomics study. This trend was successfully validated via Western

blot, where SMAD3 was downregulation as well (0.6-fold) (see Figure 17F). In the microsomal

fraction of the proteomics study, SMAD3 was only slightly regulated by 1.1-fold in response to

ΔmiR-574-5p. Western blot data showed a stronger effect although it was not significant,

revealing a tendency for an upregulation of 1.4-fold (see Figure 17G).

SMAD2, another member of the same protein family [89] [258], was also detected in the

proteomics study. In the soluble fraction, SMAD2 levels were increased 1.2-fold upon miR-

574-5p oe which was validated with a significant upregulation of 1.2-fold concerning Western

blot data (see Figure 17E). In order to complete the protein family, it was logical to analyzed

SMAD4, which was the only other SMAD detected in the proteomics study. However, neither

in the proteomics study nor in the Western Blot images, it did depict any regulation upon

ΔCUGBP1 (see Figure 17H) which validated the mass spectrometry data, but excluded

SMAD4 as interesting potential target.

Finally, p38 (Mitogen-Activated Protein Kinase 14, MAPK14) was examined as it was one of

the strongest downregulated proteins (0.3-fold) upon ΔCUGBP1 in the soluble fraction of the

proteomics study. This strong decrease of p38 protein was confirmed using Western blot

analysis which revealed a significant 0.5-fold reduction (see Figure 17I). Nevertheless, this

seems to be a secondary effect, because CUGBP1 targets are supposed to be upregulated in

response to the knockdown.

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Figure 17: Proteomics validation using Western blot analysis.

Western blot analysis (black bars) of NDUFS2 (A-D), SMAD2 (E), SMAD3 (F, G), SMAD4 (H) and p38 (I) in microsomal or soluble protein fraction of IL-1β-stimulated A549 cells upon ΔCUGBP1, ΔmiR-574-5p or miR-574-5p oe as indicated. One representative blot is shown of n=3-6 independent experiments. β-Actin was used as loading control. Quantification was performed with Odyssey Infrared Imaging System (LI-COR® Biosciences). Fold inductions compared to control are given as mean +SEM. t-test, *p ≤ 0.05. TMT ratios of the respective proteins in the proteomics study are depicted in grey bars (n=1).

In summary, these Western blot results have clearly confirmed the proteomics study. Since

the accuracy of the mass spectrometry data was assured, this allowed for further analyses of

the data.

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3.3 Physiological impact

3.3.1 Pathway analysis predicts canonical pathways, upstream regulators and

biological functions

In a next step, all proteins in soluble as well as microsomal fraction were analyzed with the

help of IPA (Ingenuity Systems, www.ingenuity.com). This tool uses the list of detected proteins

and predicts canonical pathways and upstream regulators that could also be affected.

Canonical pathways with p-values ≤ 0.05 were termed as significant. Analysis was conducted

by the group of Per-Johan Jakobsson, KI Sweden. It was revealed that the five most affected

canonical pathways in the soluble fraction were: Eukaryotic Initiation Factor 2 (eIF2) Signaling,

Regulation of Eukaryotic Initiation Factor 4 (eIF4) and 70 kDa ribosomal S6 kinase (p70S6K)

Signaling, tRNA Charging, Protein Ubiquitination Pathway and mechanistic Target of

Rapamycin (mTOR) Signaling (see Table 14; appendix). Top regulated pathways in the

microsomal fraction were: eIF2 Signaling, Protein Ubiquitination Pathway, Mitochondrial

Dysfunction, Regulation of eIF4 and p70S6K Signaling as well as Oxidative Phosphorylation.

For the upstream regulators, IPA predicted: Hepatocyte Nuclear Factor 4 Alpha (HNF4A),

Cystatin D (CST5), Rapamycin-Insensitive Companion of mTOR (RICTOR), MYC Proto-

Oncogene, MYCN Proto-Oncogene, microtubule associated protein tau (MAPT) and p53 (see

Table 15; appendix), which further underlines the influence of CUGBP1 and miR-574-5p on

cancer development. Interestingly, in the IPA prediction of influenced biological functions in

the soluble fraction, it was revealed that cancer metastasis could be highly affected by

CUGBP1 and miR-574-5p.

As presented in Figure 18, migration-related processes were on position four, seven and eight

under the top ten regulated bio functions. With help of an activation Z-score, likely regulated

molecules could be predicted based on a pattern match of up- and downregulation [259].

Further, it gives a hint in which direction the respective process is regulated. Thus, it was

predicted that migration is diminished upon ΔCUGBP1 and miR-574-5p oe, whereas ΔmiR-

574-5p cells could possibly show a higher migration potential.

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3.3.2 Influence of miR-574-5p and mPGES-1 on migratory behavior of A549 cells

Following the IPA, migration of cancer cells became an intriguing topic to work on within this

project. With two different techniques, the influence of miR-574-5p on lung cancer cell

migration was investigated. Therefore, a stable miR-574-5p oe cell line was used [196]. In so-

called wound healing or scratch assays, the ability of A549 cells to migrate into a previously

scraped scratch within the cell monolayer was studied. Therefore, cells were starved to

minimize proliferative effects. Microscope images were taken right after scratching and 24 h

later. The width of the remaining scratch was measured with ImageJ software to calculate the

migrated area. It turned out that there was no measurable difference between miR-574-5p oe

and control cells concerning their migratory behavior (see Figure 19A). However, wound

healing assays are controversially discussed in literature, as a proliferative effect can never be

fully excluded. Indeed, miR-574-5p has a positive influence on proliferation of A549 cells as

stated in [196]. Therefore, the system was switched to Boyden chamber assays. The same

stable cell line was used and seeded in transwell inlays with a porous membrane on the

bottom. Additionally, the influence of mPGES-1 was investigated. Therefore, cells were

stimulated with IL-1β to induce mPGES-1 expression, whose activity was supposed to be

repressed with the application of a selective inhibitor Compound III (CIII). Migrated cells were

stained and counted under a light microscope. However, the numbers of migrated miR-574-5p

oe and control cells were nearly the same, although high standard deviations were observed

(see Figure 19B, black bars). Moreover, stimulation with CIII did not change migration of any

of the cell lines (see Figure 19B, grey bars). Apparently, neither miR-574-5p nor mPGES-1

had any impact on migration of A549 cells.

Figure 18: Top ten regulated biological processes predicted by IPA. IPA analysis of soluble proteins from the proteomics study after ΔCUGBP1, ΔmiR-574-5p and miR-574-5p oe in IL-1β-stimulated A549 cells. Activation Z-Score indicates predicted direction of regulation.

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Figure 19: Migration assays. A549 cells with stable miR-574-5p oe or control cells were analyzed concerning migratory behavior. (A) Wound healing assay showed no difference between migrated area of miR-574-5p oe or control cells after 24 h. (B) Boyden chamber assay of IL-1β-stimulated A549 miR-574-5p oe or control cells stimulated with 10 µM Compound III (CIII) or vehicle (DMSO) revealed equal numbers of migrated cells after 5 h. Results are given as mean +SEM of n=3 different experiments.

3.4 Identification of new CUGBP1 targets

3.4.1 Western blot analysis of microsomal proteins upon ΔCUGBP1

New canonical CUGBP1 targets were determined based on their expression levels in

proteomics as well as Western blot data. It was assumed that CUGBP1 targets should exhibit

increased protein level, when the inhibitory influence of CUGBP1 is missing after knockdown.

As depicted in Figure 13, CUGBP1 protein was mainly found in the microsomal fraction. In

consequence, the focus was set on that fraction for further analyses. The previously described

CUGBP1 target and decoy target mPGES-1 served as positive control. Indeed, mPGES-1 did

show a significant 1.4-fold increased protein level in response to ΔCUGBP1 (see Figure 20A).

NDUFS2, another postulated CUGBP1 target was further analyzed via Western blot. Although

it did not show strong changes in the microsomal fraction of the proteomics study, NDUFS2

revealed a clear induction of 1.4-fold upon ΔCUGBP1 via Western blot analysis (see Figure

20B). This confirmed it as a CUGBP1 target, despite the fact that further experiments were

needed as final proof.

Since physiological experiments concerning cancer migration were conducted within this

project, associated SMAD proteins came into the focus. Therefore, the SMAD protein family

was analyzed more deeply. A variety of family members such as SMAD2, SMAD3 and SMAD4

were in fact found in the mass spectrometry data. SMAD4 did not exhibit any regulation upon

ΔCUGBP1 though, neither in the proteomics study, nor in the Western blot images. For this

reason, it was excluded as potential CUGBP1 target. The remaining SMADs however, were

promising candidates. SMAD2 and SMAD3 both exhibit multiple binding motifs all over their

3’UTRs as determined with SpliceAid 2 [256]. Moreover, Western blot analysis revealed

obvious changes in expression levels upon ΔCUGBP1. Compared to control samples, SMAD2

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was significantly increased by 4-fold in response to the knockdown (see Figure 20C). Although

this strong regulation was not measured in the mass spectrometry, the IPA also predicted that

SMAD2 expression would be enhanced. Thus, the Western blot data validated this prediction

successfully, making SMAD2 a promising CUGBP1 target. SMAD3 depicted slightly weaker

regulation in response to ΔCUGBP1, as it was upregulated by 2.2-fold according to Western

blot analysis (see Figure 20D). Again, this perfectly fitted IPA data that predicted SMAD3 levels

to be increased 2.3-fold upon knockdown of CUGBP1.

Figure 20: Protein levels of potential CUGBP1 targets in IL-1β-stimulated A549 cells with manipulated CUGBP1 levels.

A549 cells with or without ΔCUGBP1 were stimulated with IL-1β for 24 h. Microsomal fraction was analyzed via Western blot concerning (A) mPGES-1, (B) NDUFS2, (C) SMAD2 and (D) SMAD3 protein levels. One representative blot is shown of n=3-4 independent experiments. β-Actin was used as loading control. Quantification was performed with Odyssey Infrared Imaging System (LI-COR® Biosciences). Fold inductions compared to control are given as mean +SEM. t-test, ***p ≤ 0.001.

Taken together, these data provide first evidence that SMAD2, SMAD3 and NDUFS2 could be

canonical CUGBP1 targets in A549 cells.

3.4.2 Binding of CUGBP1 to mRNAs of novel canonical targets

In order to further validate the newly postulated targets SMAD2, SMAD3, NDUFS2 as well as

mPGES-1, binding of CUGBP1 to the respective mRNAs needed to be confirmed. Therefore,

RIP assays were performed. After immunoprecipitation of CUGBP1, the bound mRNAs could

be quantified via qRT-PCR. Binding of CUGBP1 was assumed, if the mRNA was enriched in

CUGBP1-IP, compared to IgG-IP. Indeed, a significant enrichment could be observed for

NDUFS2 and SMAD2 mRNAs (see Figure 21). While NDUFS2 was 2.2-fold enriched, SMAD2

showed a strong 6.4-fold enrichment compared to IgG. This confirms the interaction of

CUGBP1 with the respective mRNAs. However, SMAD3 showed no enrichment in CUGBP1-

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IP samples (see Figure 21) although it contains a binding site in the 3’UTR. This indicates that

SMAD3 mRNA is not bound by CUGBP1 under the conditions examined herein.

Overall, CUGBP1 binding to two of the three postulated targets was confirmed: SMAD2 and

NDUFS2. Since decoy targets were assumed to be a subpopulation of CUGBP1 targets, the

next step was to investigate if these new candidates were also regulated by miR-574-5p.

3.5 Identification of novel miR-574-5p/CUGBP1 decoy targets

3.5.1 Investigating a “decoy regulation pattern” via Western blot analysis

In order to find new targets of the miR-574-5p/CUGBP1 decoy mechanism, the proteomics

data were further analyzed in regard to a stringent “decoy regulation pattern” in the three

conditions: (I) ΔCUGBP1, (II) ΔmiR-574-5p and (III) miR-574-5p oe:

(I) As subpopulation of canonical CUGBP1 targets, decoy targets are supposed to be

increased in response to ΔCUGBP1 just as canonical CUGBP1 targets. With a reduced

CUGBP1 level, the inhibitory influence on gene expression of the target is missing, leading to

an upregulation on protein level (see Figure 22A). Without presence of CUGBP1, the level of

miR-574-5p should not have an impact on the decoy targets. However, decoy targets cannot

be distinguished from canonical CUGBP1 targets only by looking at the ΔCUGBP1 condition.

To clearly identify targets of the decoy mechanism, the two miR conditions are more intriguing.

(II) Potentially, decoy targets are supposed to be downregulated upon ΔmiR-574-5p. In theory,

lower intracellular levels of miR-574-5p allow higher binding capacity of CUGBP1. As CUGBP1

binding has mostly negative effects on the protein level, the respective decoy target should be

downregulated (see Figure 22B).

(III) Vice versa, miR-574-5p oe should lead to an upregulation of potential decoy targets.

Simply more miRs are accessible to prevent binding of CUGBP1 (see Figure 22C).

Figure 21: Binding of CUGBP1 to potential new target mRNAs.

RIP of A549 cells with antibodies against CUGBP1 or normal mouse IgG. Co-precipitated RNAs were quantified using qRT-PCR. mRNA quantification of NDUFS2, SMAD2 and SMAD3 in unstimulated A549 cell IP samples. Relative enrichment normalized to IgG is given as mean

+SEM of n=3-4 experiments, t-test, *p ≤ 0.05.

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Figure 22: Schematic overview of the decoy mechanism.

Expected cellular processes in the three conditions: (A) ΔCUGBP1, (B) ΔmiR-574-5p and (C) miR-574-5p oe and their effects on potential decoy targets.

In a first analysis, proteins were included even if they only had a tendency for the “decoy

regulation pattern” in the proteomics data. Such as only two conditions that met the criteria,

with the third condition potentially not matching, but also not contradicting the “decoy regulation

pattern”. For instance, High-mobility group AT-hook 2 (HMGA2) protein was strongly

upregulated upon ΔCUGBP1 (2.4-fold) and downregulated upon ΔmiR-574-5p (0.6-fold) but

showed only a 1.1-fold TMT ratio in response to miR-574-5p oe. The newly postulated

CUGBP1 target proteins and several other candidates were then analyzed via Western blot.

As positive control, the previously described decoy target mPGES-1 was used.

The analysis demonstrated that mPGES-1 protein levels did indeed react as predicted: It was

1.4-fold upregulated upon ΔCUGBP1, significantly 0.6-fold downregulated upon ΔmiR-574-5p

and 1.4-fold enriched in response to miR-574-5p oe (see Figure 23D). From the list of potential

candidates, HMGA2, Glyoxalase Domain Containing 4 (GLOD4) or PABP-Interacting Protein

2 (PAIP2) were just a few examples that were only expressed on very basal level by A549

cells. Thus, many proteins could not be detected since the sensitivity of the Western blot

system was too low compared to mass spectrometry. NDUFS2 however, was sufficiently

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expressed, although it just showed a slight tendency for the “decoy regulation pattern” in the

proteomics data. In the analyzed Western blot images, NDUFS2 levels did not change upon

manipulated miR-574-5p levels (see Figure 23A). This indicated that it is just a canonical

CUGBP1 target but not affected by the decoy with miR-574-5p.

SMAD3, although not bound by CUGBP1 in RIP assays, was still included here. As already

described, SMAD3 levels were increased upon ΔCUGBP1. Surprisingly, in the two miR

conditions SMAD3 depicted the opposite regulation than expected for a decoy target. It was

slightly upregulated upon ΔmiR-574-5p and downregulated in response to miR-574-5p oe (see

Figure 23B). The same regulation was observed for SMAD2 which showed significant

reduction in response to miR-574-5p oe (see Figure 23C). This would have been an indication

for canonical miR targets, however neither SMAD3 nor SMAD2 have a miR-574-5p seed

region in their 3’UTRs. Nevertheless, both of them were excluded as potential decoy targets.

Figure 23: Investigating a “decoy regulation pattern” via Western blot analysis.

Western blot analysis of (A) NDUFS2, (B) SMAD3, (C) SMAD2, (D) mPGES-1 in microsomal fraction of IL-1β-stimulated A549 cells upon ΔCUGBP1, ΔmiR-574-5p or miR-574-5p oe. One representative blot is shown of n=3-6 independent experiments. β-Actin was used as loading control. Quantification was performed with Odyssey Infrared Imaging System (LI-COR® Biosciences). Fold inductions compared to control are given as mean +SEM. t-test, *p ≤ 0.05, ***p ≤ 0.001.

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Taken together, for none of the detectable proteins the “decoy regulation pattern” could be

elucidated via Western blot analysis. Neither SMAD2, SMAD3 or NDUFS2 showed the

adequate pattern concerning the two miR conditions (see Figure 23). While other candidates,

such as GLOD4 or HMGA2 which were found in the mass spectrometry data, were not

detectable via Western blot. Thus, only mPGES-1 was left as CUGBP1/miR-574-5p decoy

target.

3.5.2 Stringent “decoy regulation pattern” in the proteomics study

As the rather loose criteria in the first approach led to inconclusive results, the proteomics data

were re-analyzed with more stringent criteria to find if the decoy mechanism is as global as

expected. A closer look was taken on how many proteins exactly depicted the perfect “decoy

regulation pattern” i.e. showed an upregulation of ≥1.5-fold in response to ΔCUGBP1, a

downregulation of at least 0.5-fold after ΔmiR-574-5p and at least an upregulation of ≥1.5 fold

in response to miR-574-5p oe.

It was revealed that, as expected, in the soluble fraction zero proteins matched all three criteria

(see Figure 24A). This further underlines the hypothesis that the decoy mechanism mainly

takes place in the microsomal fraction. Concentrating on the microsomal fraction, from

originally 3970 proteins, only seven proteins (0.2%) matched this perfect “decoy regulation

pattern” in all three conditions (see Figure 24B):

Ubiquitin Conjugating Enzyme E2 R2 (UBE2R2)

Centrosomal Protein of 41 kDa (CEP41)

RNA polymerase-associated protein LEO1 (LEO1)

General Transcription Factor IIE Subunit 2 (GTF2E2)

Polyadenylate-Binding Protein-Interacting Protein 2 (PAIP2)

Solute carrier family 39 member 6 (SLC39A6)

GRIP1 Associated Protein 1 (GRIPAP1)

Of note, mPGES-1 was not under the seven proteins, since it did not depict the exact pattern

in the proteomics data either. Although it displayed the “decoy regulation pattern” in Western

blot images, it was not so prominent in the mass spectrometry data. This underlines that mass

spectrometry data need to be validated, as artefacts can be detected and it therefore not

necessarily reveals an accurate representation of the actual proteome within the cells.

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Figure 24: Proteins with a stringent “decoy regulation pattern” in the proteomics study.

Venn diagram with numbers of proteins that exhibit a very stringent “decoy regulation pattern” in (A) soluble and (B) microsomal fraction of the proteomics study. ↑ indicates increased protein level, ↓ indicates decreased protein level.

This small number of proteins provided first evidence that the CUGBP1/miR-574-5p decoy

seems to be a very specific mechanism and does not affect an extensive amount of proteins.

3.5.3 Binding analysis of potential decoy targets

The seven potential decoy targets were then further analyzed concerning binding behavior of

CUGBP1. GRIPAP1 was the only exception, as it has no described 3’UTR according to

ensemble database. The remaining six 3’UTRs were analyzed concerning CUGBP1 binding

motifs [256]. It was found that all of them had at least one binding site in their 3’UTR.

Subsequently, binding of CUGBP1 to the mRNAs was investigated by RIP assay. It turned out

that none of the mRNAs was enriched in the CUGBP1-IP compared to mock control (see

Figure 25).

Figure 25: Binding of CUGBP1 to potential decoy targets. RIP of A549 cells with antibodies against CUGBP1 or normal mouse IgG. Co-precipitated RNAs were quantified using qRT-PCR. mRNA quantification of UBE2R2, CEP41, LEO1, GTF2E2, PAIP2 and SLC39A6 in unstimulated A549 cell IP samples. Relative enrichment normalized to IgG is given as mean +SEM of n=3 experiments.

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Although it cannot be excluded that one of the seven mRNAs is bound under different

conditions or in a different cell type, they were excluded as interesting candidates.

3.6 Subcellular localization of CUGBP1 and miR-574-5p in A549 cells

Since the search for new decoy targets seemed to have an end at that point of the study, a

different approach was needed to further characterize the decoy mechanism. As a next step,

it was necessary to elucidate where exactly in the cell the interaction of CUGBP1 and miR-

574-5p takes place. Therefore, subcellular localization of the two binding partners in A549 cells

was visualized by performing immunofluorescence staining and FISH assay, respectively.

Localization of miR-574-5p was determined by using a specific DIG-labeled LNA probe and a

DIG binding antibody. Whereas immunofluorescence staining with a specific primary antibody

was performed in order to localize CUGBP1. As control, the well-established nuclear marker

DAPI was used. Interestingly, with these techniques it was demonstrated that both, CUGBP1

and miR-574-5p were mainly located in the nuclei of A549 cells (see Figure 26). Nearly no

cytosolic signals were detected for miR-574-5p, which is quite unusual for a miR. CUGBP1

exhibited stronger cytoplasmic signals but was still predominantly located in the nucleus. IL-1β

stimulation had no influence on subcellular localization of both CUGBP1 and miR-574-5p. All

staining experiments were kindly performed by Julia Wellstein using established protocols.

Figure 26: Subcellular localization of CUGBP1 and miR-574-5p in A549 cells.

(A) FISH assay and (B) immunofluorescence staining of A549 cells with or without IL-1β-stimulation for 24 h. miR-574-5p FISH was performed using DIG-labelled miRCURY LNA probes (green). Immunofluorescence staining with specific antibody visualized subcellular localization of CUGBP1 (red). Nuclei were counterstained with DAPI (blue). Scale bar = 10 µm. One representative image of n=3 independent experiments is shown.

Hence, it was concluded that the decoy mechanism mainly takes place in the nucleus,

especially during the experiments performed herein. In case miR-574-5p interfered solely with

CUGBP1’s nuclear functions, this would shift the focus of this project from protein level

regulation more to the direction of splicing regulation.

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3.7 Bioinformatical analysis of 3’UTR splicing patterns

The main nuclear function of CUGBP1 is regulation of AS. Thus, it was consequential to

continue with investigations concerning splicing. As the only known decoy target of CUGBP1

and miR-574-5p, mPGES-1 served as model, especially since it represents a very unique case.

CUGBP1 binds to two binding sites in the mPGES-1 3’UTR and influences AS which creates

a shorter 3’UTR isoform [196]. This was used as a paradigm, meaning other potential decoy

candidates were analyzed in regard of this specific splicing pattern. For this comprehensive

analysis, all 399 proteins which showed an upregulation in response to ΔCUGBP1 in soluble

or microsomal fraction of the proteomics study were included. 3’UTR sequences from all

described isoforms were downloaded, using ensemble biomart (GRCh38/.p12, version 91,

[252]). This resulted in a list of 1916 3’UTR sequences which were then aligned with 42

described CUGBP1 binding motifs (see Table 10, using the online tool Splice Aid F [109]).

Table 10. List of potential binding motifs of CUGBP1 No. Motif Length (nt)

1 CUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUGCUUG

151

2 CAGACAGACAGACAGACAGACAGACAGACAGACAGACAGACAGACAGACAGACAGACAGACAGACAGACAGACAGACAGACAGACAGACAGACAGACAGACAGACAGACAGACAGACAGACAGACAGA

127

3 UCUGUCUGUCUGUCUGUCUGUCUGUCUGUCUGUCUGUCUGUCUGUCUGUCUGUCUGUCUGUCUGUCUGUCUGUCUGUCUGUCUGUCUGUCUGUCUGUCUGUCUGUCUGUCUGUCUGUCUGUCUGUCUG

127

4 CAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAGCAG

122

5 CGGACGGACGGACGGACGGACGGACGGACGGACGGACGGACGGACGGACGGACGGACGGACGGACGGACGGACGGACGGACGGACGGACGGACGGACGGACGGACGGACGGACGGACGGA

119

6 UCCGUCCGUCCGUCCGUCCGUCCGUCCGUCCGUCCGUCCGUCCGUCCGUCCGUCCGUCCGUCCGUCCGUCCGUCCGUCCGUCCGUCCGUCCGUCCGUCCGUCCGUCCG

107

7 CCGCCGCCGCCGCCGCCGCCGCCGCCGCCGCCGCCGCCGCCGCCGCCGCCGCCGCCGCCGCCGCCGCCGCCGCCGCCGCCGCCGCCGCCG

90

8 CGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGG

90

9 CCCGCCCGCCCGCCCGCCCGCCCGCCCGCCCGCCCGCCCGCCCGCCCGCCCGCCCGCCCGCCCGCCCGCCCGCCCGCCCGCCCG

84

10 UGUGUGUGUGUGUGUGUGUGUGUGUGUGUGUGUGUGUGUGUGUGUGUG 48

11 CUGCUGCUGCUGCUGCUGCUGCUGCUGCUGCUG 33

12 UGUGUGUGUGUGUGUGUGUGUGUGUGUUUUU 31

13 CUCUCUCUCUCUCUCUCUCUCUCUCUCUCU 30

14 CUGCUGCUGCUGCUGCUGCUGCUGCUGCUG 30

15 GUUGGUUGGUUGGUUGGUUGGUUGGUUGGU 30

16 UAUGUAUGUAUGUAUGUAUGUAUGUAUGUA 30

17 UGGUGGUGGUGGUGGUGGUGGUGGUGGUGG 30

18 UGUGUGUGUGUGUAUGUGUGUGUGUGUGUG 30

19 UGUGUGUGUGUGUGUGUGUGUGUGUGUGUG 30

20 UUGUUGUUGUUGUUGUUGUUGUUGUUGUUG 30

21 UGUGUGUGUGUGUGUGUGUGUGUGUUUUU 29

22 CAUGCAUGCAUGCAUGCAUGCAUGCAUG 28

23 CCUGCCUGCCUGCCUGCCUGCCUGCCUG 28

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24 UAUGUAUGUAUGUAUGUAUGUAUGUAUG 28

25 UGUGUGUGUGUGUGUGUGUGUGUUUUU 27

26 GUGUUUGUGUUUGUGUGUGUUUGUU 25

27 CUGCUGCUGCUGCUGCUGCUGCUG 24

28 CUGCUGCUGCUGCUGCUGCUGCUG 24

29 GUUUGUUUGUUUGUUUGUUUGUUU 24

30 UUUUUUUGUUGUGUUUUUUCCUU 23

31 UUUCUUGUUUGUUUGUUUGGGU 22

32 UGUUGUGUGUGUGUGU 16

33 UGUGUUGUGUGU 12

34 UGUGUGUGUGU 11

35 UGUGUGUGUG 10

36 UGUGUGUGU 9

37 UGUGUGUG 8

38 CUGUCUG 7

39 UGUGUGU 7

40 UGUUUGU 7

41 UGUGUG 6

42 UGUGU 5

According to Splice Aid F; used in the bioinformatical 3’UTR analysis [109].

In a first step, a high stringency analysis was performed, looking for the exact splicing pattern

of mPGES-1 (see Figure 27A). However, it turned out that none of the transcripts exhibits this

exact pattern with two very long binding sites and a 1000 nt intron in between. Therefore, it

was considered that the splicing pattern might not be identical.

In a second low stringency analysis, criteria were loosened, screening for transcripts

containing 2 or more binding sites of at least 8 nt length and with at least 100 nt in between

(see Figure 27B).

Figure 27: High stringency and low stringency approach in bioinformatical 3‘UTR analysis. All 3’UTRs of potential CUGBP1 targets were analyzed concerning a (A) high stringency/ mPGES-1 splice pattern or (B) low stringency pattern with two or more CUGBP1 binding sites (≥ 8 nucleotides), with potential intron (≥ 100 nucleotides).

As listed in Table 11, 575 transcripts resembling 30% of the originally 1914 3’UTRs, contained

a CUGBP1 binding site. Restricting it to binding sites that were 8 nt or longer, only 118

transcripts (6%) were left, as a lot of them were shorter (see number 38-42 in Table 10). By

applying the criterion that at least 2 distinct binding sites of at least 8 nt length should be in the

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sequence, only 33 transcripts were left (1.7%). Eventually, by looking for an additional intron

of at least 100 nt in between the binding sites, only 11 transcripts (0.6%) fulfilled all the criteria.

Due to the binding motifs of CUGBP1 which often are repeats of only two nt (e.g. UG/TG) the

last step also removed transcripts that had several UG-repeat binding motives but only 2 nt

apart, meaning that these were actually the same binding sites. For instance, the 3’UTR of

Lysosome-associated membrane protein 2 harbors a 40-nt UG-repeat, which is why it was

predicted to have 17 CUGBP1 binding sites (of number 37, Table 10). However, when looking

at the exact positions within the 3’UTR sequence, it becomes clear that these predicted distinct

binding sites are at position 420, 422, 424 etc., belonging to the same 40-nt UG-repeat.

Therefore, the last criterion was not only used to find potential introns but also to support the

second criterion of two distinct binding sites.

Table 11. Number of transcripts fulfilling the low stringency analysis criteria.

Criterion I Criterion II Criterion III

Binding site length Number of binding sites

Space between binding sites

Number of transcripts left

All All All 575

binding site of ≥8 nt All All 118

binding site of ≥8 nt ≥2 binding sites All 33

binding site of ≥8 nt ≥2 binding sites ≥ 100 nt intron 11

Those 11 transcripts that were finally left, belong to 9 different genes (see Figure 28, and Table

17; appendix)

MAF BZIP Transcription Factor K (MAFK)

UBX Domain Protein 2B (UBXN2B)

Complexin 2 (CPLX2)

UBE2R2

Superoxide dismutase 2 (SOD2)

CUB domain-containing protein 1 (CDCP1)

CEP41

RNA Binding Motif Protein 23 (RBM23)

SET Domain Containing Lysine Methyltransferase 7 (SETD7)

In ten of the 3’UTRs, exactly two CUGBP1 binding sites were found, while the 3’UTR of CPLX2

harbors three distinct binding sites. However, the first and the second one at position 1233 and

1250 do not span a potential intron of ≥ 100 nt. Binding motifs were mostly 8 nt long (number

37 in Table 10), except for CEP41 which harbors a 9 nt binding motif (number 36 in Table 10)

and SOD2 which harbors a 11 nt binding motif (number 34 in Table 10). The potential introns

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in between the two binding sites range from rather small 199 nt for CEP41 up to 7537 nt for

SOD2 (for more details see Table 17; appendix).

Figure 28: Transcripts from low stringency 3’UTR analysis.

Transcripts which harbor two (in case of CPLX2 three) CUGBP1 binding sites in the 3’UTR. For more details, see Table 17; appendix. All transcripts are protein coding except for MAFK ENST00000343242 which is predicted to be an NMD target. Downloaded from ensemble Human (GRCh38.p12).

Of these, only CEP41 and UBE2R2 depicted the “decoy regulation pattern” in the mass

spectrometry data. However, they were not bound by CUGBP1 in the RIP assay of A549 cells

(see Figure 25) and therefore excluded as novel decoy targets. This small number of proteins,

together with the fact that the high stringency analysis did not provide any hits, strongly

indicated that it is a very exceptional splice pattern and the decoy mechanism must be very

specific.

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4. Discussion

When it was revealed in 2010 that miRs can act as decoy to RBPs [193], this opened up a

whole new field of miR research. Recently, the decoy mechanism was also described for

CUGBP1 and miR-574-5p [196]. The resulting regulation of mPGES-1 expression and PGE2

levels in non-small cell lung cancer gives hope for the development of novel therapeutic

approaches and might even allow the use of miR-574-5p as a biomarker for lung cancer patient

stratification.

4.1 Insights into the proteome of A549 lung cancer cells

A mass spectrometry-based proteomics study was conducted in order to elucidate existence

and global distribution of novel targets of CUGBP1 and miR-574-5p. To this end, A549 lung

cancer cells with manipulated levels of CUGBP1 or miR-574-5p were used. Of note, the

proteomics study was used as shotgun procedure. It gives first indications which proteins might

be regulated but does not always depict the exact expression changes. The regulation of

mPGES-1 is a simple example for that. It was not regulated upon ΔCUGBP1 in the mass

spectrometry data, although it is a described CUGBP1 target. Therefore, it was necessary that

any information drawn from the data set was validated. Experimentally, the regulation of

distinct proteins was successfully validated via Western blot analysis. Moreover, published

data sets of CUGBP1 and miR-574-5p targets were compared with lists of potential targets

from the proteomics study. For instance, a published Clip Seq (cross-linking

immunoprecipitation-high-throughput sequencing) data set of Tonqueze et al. revealed a 25%

overlap (102 proteins) with potential CUGBP1 targets [260]. For canonical miR-574-5p targets,

a similar approach was conducted: 130 (20%) of them were also found in a list of predicted

targets from the database targetscan.org [261]. Finally, one of the strongest downregulated

proteins upon ΔCUGBP1 in the microsomal fraction was CUGBP1 itself (-3.1-fold), which

validates the knockdown as well as the mass spectrometry data (see Table 16). With that the

accuracy of the proteomics study was assured, both experimentally and by comparison to

literature and databases.

In order to broaden the insights in compartment specific post-transcriptional regulation,

proteins from soluble and microsomal fraction were separately analyzed in the proteomics

study [250]. It is generally assumed that soluble proteins are translated from free ribosomes in

the cytosol, whereas the microsomal fraction is associated with ribosomes of the rough

endoplasmic reticulum [262]. Indeed, differential regulation between fractions was observed

for many proteins. Concerning mass spectrometry data, 78 proteins were observed showing

opposite regulation in soluble and microsomal fraction, thereof 48 in the two miR conditions

Discussion __________________________________________________________________________

70

and 30 in the CUGBP1 condition. Using Western blot analysis during proteomics validation,

the R-SMADs SMAD2 and SMAD3 were also observed to show opposite regulation in the two

fractions. This underlines the theory that RBPs may allow different translational efficiencies

concerning free and endoplasmic reticulum-bound ribosomes [263] [264].

The microsomal fraction appears to be of greater importance compared to the soluble fraction.

On the one hand, there was a broader diversity of proteins detected (60% more different

proteins than in the soluble fraction). Compared to other proteomics studies, this result is

unusual. Since its establishment in 2008, the microsomal fractionation protocol has been used

in a variety of proteomics studies [250]. For monocytic MM6 cells and A549 lung cancer cells,

it was demonstrated in the last years that the soluble and microsomal fractions contained

roughly the same amount of different proteins [265] [195] [266]. Thus, the discrepancy to this

thesis could be potentially caused by technical variances such as differences in digestion

protocol, labelling and buffer composition [265].

Another point to consider is that there was a higher percentage of microsomal proteins that

actually revealed a regulation in response to the oe or knockdowns. An explanation could be

that CUGBP1 exerts its functions more in the microsomal protein fraction. This hypothesis is

supported by the fact that CUGBP1 protein itself can be found predominantly in the microsomal

fraction as confirmed by Western blot analysis. Furthermore, it was previously shown that the

decoy targets of miR-328 and hnRNP E2 are also predominantly found in the microsomal

fraction of MM6 cells [195].

In conclusion, it can be assumed that effects of CUGBP1 and the decoy with miR-574-5p are

more prominently found in the microsomal protein fraction. Hence, special emphasis was

placed on this fraction in the course of this project.

4.2. Discovery and verification of new canonical CUGBP1 targets

In order to screen for potential new targets of CUGBP1, different approaches were combined.

Concerning the mass spectrometry data, around 72% of the upregulated proteins were found

to have a CUGBP1 binding site. Comparing this to total protein amounts, 4.5% of all detected

proteins could potentially be CUGBP1 targets. This fits the general assumption that CUGBP1

is a multifunctional RBP, as a lot of transcripts contain its binding motif. Nevertheless, CUGBP1

is described to bind those potential targets strictly context specific [267]. Not only the binding

alone but also the outcome is context specific, as CUGBP1 can influence a variety of mRNA

processing steps, from AS [111] [112] in the nucleus to translation in the cytoplasm [133] [134]

[135]. Furthermore, these assumptions are based on the repressive functions of CUGBP1. In

specific cases CUGBP1 was also observed to have a positive impact on its targets [136] [137].

Therefore, the downregulated proteins in response to ΔCUGBP1 could potentially also be

Discussion __________________________________________________________________________

71

positively regulated by CUGBP1. Moreover, CUGBP1 binding sites were only analyzed in

3’UTR sequences within this study. This was consequential, as most of the time CUGBP1 is

described to bind to 3’UTRs of target mRNAs and additionally mPGES-1 was used as a model

for a decoy target. Of note, in some rare cases CUGBP1 was also observed to bind to exon-

intron boundaries within the CDS [268] or 5’UTR sequences [269] [270]. Therefore, the actual

number of targets might be even higher.

The NADH-ubiquinone NDUFS2 was mentioned as potential CUGBP1 target in HeLa cells for

the first time in a study by Rattenbacher et al. [84]. NDUFS2 is part of the core subunit of the

mitochondrial membrane respiratory chain NADH dehydrogenase (mitochondrial complex I)

[271] [272]. This complex is an assembly of at least 43 different subunits, while NDUFS2

belongs to a group of 7 iron-sulfur metalloproteins which stabilize the early intermediate

complex [273]. In order to investigate the diverse binding motifs of CUGBP1, Rattenbacher et

al. performed immunoprecipitation and subsequently microarray analysis. This resulted in a

list of 613 potential CUGBP1 targets including their specific sequences. NDUFS2 was

identified as one of 477 transcripts that were solely found in the CUGBP1-IP. With a short

3’UTR of only 202 nt, NDUFS2 contains a 31 nt long GU-repeat at position 77 that serves as

binding site for CUGBP1. This motif was additionally present in 17% of the other detected

transcripts in the study and has a 7% prevalence in the whole genome [84]. Furthermore, they

could show that by binding of CUGBP1, the NDUFS2 mRNA as well as any other mRNA

containing the specific motif was rapidly degraded, hinting towards a CUGBP1-mediated decay

mechanism. This hypothesis does also match the results within this thesis. In A549 cells,

CUGBP1 also seems to have a negative impact on NDUFS2 gene expression. However,

NDUFS2 did not show the “decoy regulation pattern”. Therefore, it cannot be considered as

decoy target but only as canonical CUGBP1 target. It is described that mutations in the

NDUFS2 gene go along with the autosomal recessive Mitochondrial Complex I Deficiency

[274] [275] [276]. This heterogeneous disease can have a variety of phenotypes from lethal

neonatal disease to adult-onset neurodegenerative disorders, cardiomyopathy or impaired

liver function [277] [278] [279]. By influencing a protein of the respiratory chain, it can be

assumed that CUGBP1 could have an impact on cellular energy supply as well as associated

diseases.

Another protein family that was investigated are the SMADs. Functionally, they act as signal

transducers for the Transforming growth factor (TGF)-β signaling pathway. They can be

divided in the so-called receptor (R-) SMADs (SMAD1-3, SMAD5 and SMAD8-9), the Co-

SMAD (SMAD4) and the inhibitory (I-) SMADs (SMAD6-7) [280] [281] [282]. Upon TGF-β

receptor activation, two R-SMADs get phosphorylated by the receptor kinase and build a

complex with a Co-SMAD [283] [284]. The trimer then translocates to the nucleus, where it

Discussion __________________________________________________________________________

72

acts as transcription factor. As a result, upregulation of certain response genes like Snail

Family Transcriptional Repressor 1 (Snail1), Zinc Finger E-Box Binding Homeobox 1 (ZEB1)

and Twist Family BHLH Transcription Factor 1 (Twist1) [285] [286] [287] has been observed.

As those are all associated with EMT, SMAD proteins are considered being essential for tumor

metastasis throughout a variety of cancer types [288].

Thus, SMAD proteins were an interesting objective within this project, as metastasis was

predicted by IPA and also some SMADs were detected in the proteomics study. It was verified

that SMAD2 is a canonical target of CUGBP1. SMAD3 was another promising candidate, but

the mRNA was not bound by CUGBP1, at least under the conditions investigated here. This

does not necessarily exclude SMAD3 as CUGBP1 target. Potentially, binding of SMAD3

mRNA only occurs under certain conditions e.g. with a certain stimulus like TGF-β, some

culture conditions like starvation or simply in another cell type [267]. Interestingly, another

family member, SMAD7 was not detected in the proteomics study. Nevertheless, it is described

as CUGBP1 target in C2C12, a murine myoblast cell line [110]. In a publication by Lee et al.

RIP assays proved the interaction of CUGBP1 with SMAD7 mRNA. It was further elucidated

that binding led to a destabilization of the SMAD7 transcript, while knockdown of CUGBP1

increased the stability of the mRNA [110]. This fits to the general assumption that CUGBP1 is

a destabilizing factor. In A549 cells, SMAD7 is barely expressed, which might explain why it

was not detected in the proteomics study. However, it is interesting that several members of

the SMAD family seem to be affected by CUGBP1. In a similar way, this is already described

for the signal recognition particles (SRP) [117] which underlines the existence of CUGBP1

regulons.

4.3 Decoy target search

One of the main aims of this project was it to investigate if the decoy mechanism affects other

targets of CUGBP1 or if it is strictly specific for mPGES-1. In that regard, a first approach was

it to screen for a “decoy regulation pattern” on protein level.

(I) CUGBP1 has generally negative effects on its targets, based on its role as a translational

repressor or decay mediator. Therefore, it can be hypothesized that the majority of decoy and

canonical CUGBP1 targets should react to the knockdown with an increased protein level.

(II) Taking a closer look at the miR-574-5p oe condition, it becomes clear that this also should

have a positive effect on protein levels of the decoy targets. Similar to the ΔCUGBP1 condition,

little to no CUGBP1 can bind to the binding sites as miR-574-5p acts as competitive inhibitor

and sequesters CUGBP1 away from its target. Hence, an increased mPGES-1 protein level

can be observed.

Discussion __________________________________________________________________________

73

(III) In the ΔmiR-574-5p samples, CUGBP1 can easily bind its targets. Decoy target proteins

were expected to be decreased compared to control, while canonical CUGBP1 targets should

not be affected by the miR-574-5p levels. In the case of mPGES-1, binding of CUGBP1 leads

to a lower amount of the 3’UTR splice variant and thus to a lower protein level.

When the proteomics data were analyzed concerning stringent criteria for this “decoy

regulation pattern”, it was revealed that only 7 microsomal proteins (0.1% of total proteins) did

resemble this perfect pattern that matched all three criteria. In Western blot analyses, no

proteins were found to reveal this “decoy regulation pattern” except for mPGES-1. The

analyzed CUGBP1 targets NDUFS2, SMAD2 and SMAD3 did also not show the expected

pattern. Unfortunately, the Odyssey Western blot system provided too little sensitivity for the

detection of some weakly expressed proteins like HMGA2. Nevertheless, concerning the

decoy target search on protein level it could be assumed that the decoy mechanism does not

have a global impact on A549 proteome. This was especially intriguing, as it was initially

assumed that the decoy mechanism would influence the expression of a variety of genes. In a

recent study, it was shown that numerous proteins could be regulated by the decoy function of

miR-328 and hnRNP E2 [195]. Therefore, it would have been reasonable that the miR-574-

5p/CUGBP1 decoy would also have a global impact. However, the data within this thesis

indicate the opposite.

Overall, it can be assumed that the CUGBP1 and miR-574-5p decoy mechanism would not

promiscuously affect a lot of proteins.

4.4 Bioinformatical 3’UTR analysis revealed unique splice pattern

As it was demonstrated that both CUGBP1 and miR-574-5p were located predominantly in the

nucleus, the CUGBP1 function as splicing factor came into focus. It was reasonable, as it was

already described that mPGES-1 mRNA was alternatively spliced [196]. In fact, mPGES-1

mRNA is spliced in the 3’UTR creating a shorter isoform. Thereby, a 3’UTR ALU element is

removed as well as a myriad of miR and RBP binding sites which allow for a higher translational

efficiency [196]. It is generally known that shorter 3’UTRs are beneficial for a higher expression

profile due to a diminishment of binding sites [289].

In a bioinformatical 3’UTR analysis it could be shown that the 3’UTR splicing pattern of

mPGES-1 was not found in any other transcript and therefore was termed as being unique.

This outcome was expected, as it was unlikely to find another 3’UTR with one 39 nt and one

46 nt CUGBP1 binding motive, with a 1000 nt intron in between. In a low stringency analysis,

it was screened for a similar pattern with two shorter CUGBP1 binding sites and with only 100

nt in between. The resulting list of only 11 transcripts (0.5% of the analyzed transcripts) gave

an additional hint that this was a quite specific pattern.

Discussion __________________________________________________________________________

74

Of note, this approach only aimed to find potential other splice patterns in 3’UTRs, as the

3’UTR splicing of mPGES-1 was used as a model. Conventional splicing processes were not

considered herein, although CUGBP1 does also influence (alternative) splicing in the CDS

[111] [112]. Further, it has to be mentioned that CUGBP1 cannot only bind in 3’UTR sequences

but also in CDS and 5’UTRs [268] [269]. Therefore, this bioinformatical study was just a first

approach and needs to be followed by additional analyses like bioinformatical extension to

CDS or experimental analysis of splice events.

Moreover, as two of the low stringency transcripts were not bound by CUGBP1 in RIP assays,

further experiments need to be conducted. It is likely that with an additional stimulus or other

cellular conditions the binding of CUGBP1 could be triggered. Unfortunately, as CUGBP1

binds strictly context specific and is described to function in various steps of mRNA processing

this would have exceeded this project.

Overall, the screening for new decoy targets using several different approaches did not result

in any conclusive hits. This highly complex proteomics study provided a broad insight into the

proteome of A549 lung cancer cells, including compartment-specific regulation and new

canonical targets of CUGBP1 but did not provide any new targets regulated by the interaction

of CUGBP1 and miR-574-5p. Hence, it is assumable that in A549 cells mPGES-1 is the only

protein regulated by the CUGBP1/miR-574-5p decoy mechanism.

4.5 Physiological impact

In order to investigate the overall physiological functions of CUGBP1, miR-574-5p and the

decoy mechanism, a pathway analysis was performed that predicted biological functions,

canonical pathways and upstream regulators based on the mass spectrometry data. As

metastasis and migration were among the top ten predictions, it was an intriguing research

topic.

4.5.1 Influence of miR-574-5p on metastasis

In wound healing and trans-well assays, surprisingly, no significant effects were measured with

miR-574-5p oe cells compared to control cells. This clearly contradicts literature, where miR-

574-5p was described as being pro-metastatic in small cell and non-small cell lung cancer

cells, respectively [209] [210]. In these two publications from the same research lab, they

investigated the influence of miR-574-5p on the migration of A549 cells with the same two

methods described within this thesis but with a different outcome. However, there were

differences in the procedures. For the wound healing assays within this project, cells were pre-

starved, before the actual assay in order to minimize proliferation of the cells. This step was

Discussion __________________________________________________________________________

75

skipped in the publications by Zhou et al.. Further, they performed trans-well assays for 24 h.

This opens up the possibility of further proliferation of the migrated cells on the bottom of the

membrane, as A549 cells are described to have a doubling time of around 18-22 h [290].

Indeed, transient miR-574-5p oe has a pro-proliferative effect on A549 cells [196] which could

explain why they measured enhanced number of cells which potentially were falsely

considered as migrated cells.

Another possible explanation could be that within this project a stable miR-574-5p oe cell line

was used. Compared to a transient transfection, stable integration is dependent on position,

copy number and site of integration. Potentially the viral particle transduction could have

caused other variances in the cell, besides the miR-574-5p oe and therefore caused a different

migratory behavior. An example that underlines this theory is that A549 cells transiently

overexpressing miR-574-5p show an increased proliferation, while stable miR-574-5p oe cells

do not [291]. In order to exclude those variances, it would be necessary to repeat the wound

healing and trans-well assays with a transient overexpression or knockdown of the miR.

Another point to mention is that IPA predicted metastasis to be diminished with miR-574-5p

oe, which does not fit the results of the migration assays and additionally stands in contrast to

the two publications about the enhancing effects of miR-574-5p on migration [209] [210].

However, in this context, it has to be mentioned that the IPA is based on the mass spectrometry

results which were performed with transient miR-574-5p oe (300-fold oe), while the migration

assays were performed with a stable miR-574-5p oe (15-fold oe) [196].

What supports the IPA is the fact that it also predicted metastasis to be decreased upon

ΔCUGBP1. Especially metastasis of breast cancer cells was predicted in that context. This

underlines the recently published positive impact of CUGBP1 on EMT-proteins and tumor

metastasis of breast cancer cells [136] [137].

Overall, the migration assays do not give any conclusive results and further experiments are

needed to exclude any influence of miR-574-5p and CUGBP1 on tumor metastasis.

4.5.2 Influence of mPGES-1 on metastasis

In a further approach, the influence of mPGES-1 was investigated. A previous study showed

that the pro-proliferative effect of miR-574-5p was solely caused by its influence on mPGES-1.

Therefore, this was also an interesting point to investigate in the context of tumor migration.

The fact that mPGES-1 did not affect migration in the experiments within this thesis is

controversial. Two publications describe mPGES-1 as promoter of cancer metastasis in vivo

[292] [293]. Both injected tumor cells intravenously in wild type or mPGES-1 knockout mice

and determined metastases of the lung by number and weight of the whole organ. Although

Discussion __________________________________________________________________________

76

this is a common procedure, it does not fully cover the physiological background of tumor

metastasis.

In general, to undergo metastasis the cancer cells do have to go through a process called EMT

[294]. It includes an extensive phenotypical change from an epithelial to a more mesenchymal

cell type. Thereby, the cells lose their cell-cell-adhesion and acquire migratory abilities,

accompanied by a mayor change in the protein profile. This transition is crucial for invasion,

meaning that the tumor cells gain the ability to enter the vascular system. Only then, the cells

can migrate to distant sites within the body. Then, the migrated cells have to undergo the

reverse process, called mesenchymal epithelial transition to regain their adhesive properties

and form new metastases [295] [296].

By injecting tumor cells directly in the blood system, the metastasis process is tremendously

facilitated. Further, in vivo models are only basally comparable with trans-well and wound

healing assay in cell culture systems. Nevertheless, it is odd that mPGES-1 does not render

migration of the lung cancer cells at all. Therefore, it could be interesting to perform further

experiments in that regard. For example, invasion assays with 3D cell culture would provide a

more physiological environment and potentially could reveal new insights.

4.6 Outlook

This thesis provided first evidence that the decoy mechanism of miR-574-5p and CUGBP1

could be specifically regulating mPGES-1. Using different screening approaches, no other

target could be found that was regulated by the interaction of the miR and the RBP.

However, the decoy mechanism itself is still not fully understood. In the future it could be an

intriguing research topic to investigate how the interaction of miR-574-5p and CUGBP1 is

triggered. Until now it is described to happen in the presence of IL-1β. What exactly causes

miR-574-5p to bind to CUGBP1 still remains unclear. The phosphorylation status of CUGBP1

could be the key, as it is a known regulator of CUGBP1 function [120] [121] [122] [123] [119].

Further, the existence of binding partners or chaperones could also be possible. Additionally,

the exact impact of IL-1β is of great interest. Signaling downstream of the IL-1β receptor

involves recruitment of myeloid differentiation primary response gene 88 (MyD88) and/or Toll-

interleukin receptor domain containing adaptor-inducing interferon-β (TRIF) [297].

Interestingly, miR-574-5p is also described to act as TLR8 ligand and activate this exact

signaling pathway, which provides a promising connection [184] [185] [186] [187].

Finally, specificity of the decoy mechanism is an interesting question. Until now, the

mechanism is only described for two pairs: miR-328/hnRNP E2 and miR-574-5p/CUGBP1

[193] [196]. It would be interesting to investigate if there are more miR/RBP pairs or if the given

ones can also interact with other partners. CUGBP1 could potentially bind other GU-rich miRs.

Discussion __________________________________________________________________________

77

Vice versa, miR-574-5p is described to be bound by Transactive response DNA binding

protein43 kDa (TDP-43) [298], an RBP which facilitates pri-miR processing and builds a

complex with Drosha [299]. Further GU-binding RBPs are Nova-1 [300] and CUGBP2 [301]

which could also potentially bind to miR-574-5p. Clearly, future experiments are needed to fully

elucidate this topic.

The fact that the miR-574-5p/CUGBP1 decoy is specific for mPGES-1 was initially surprising.

Eventually, the discovery of the unique 3’UTR splice pattern provided a possible explanation.

Moreover, it has to be taken into account that the miR-574-5p effects on tumor progression

were also based solely on mPGES-1 [196]. This was probably the first hint for this kind of

specificity. Although one can never fully exclude each and every potential option, the results

within this thesis clearly support the postulation that mPGES-1 is the only protein regulated by

the miR-574-5p/CUGBP1 decoy in A549 cells. This would open up new options for NSCLC

patients. It is well-known that not all patients benefit from a treatment with medication that aims

to reduce PGE2 levels [248] [249]. Presumably, because not all lung adenocarcinomas are

dependent on PGE2. Therefore, the miR-574-5p levels could serve as stratification marker to

identify the subgroup with high mPGES-1 and PGE2 levels. For those patients, treatment with

NSAIDs could be a beneficial tool in the fight against NSCLC.

References __________________________________________________________________________

78

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Appendix

Abbreviations

µL Microliter 5’ m7G 5’ 7-methylguanylate cap AGO Argonaute protein An 3’ 3' Pol-A tails ARE AU-rich element AS Alternative splicing ATCC American Type Culture Collection BACE1 β-site of APP cleaving enzyme BCA Bicinchoninic acid assay BSA Bovine serum albumine C. elegans Caenorhabditis elegans CDCP1 CUB domain-containing protein 1 CDH1 Cadherin-1 CDS Coding sequence CEBPA CCAAT/enhancer-binding protein alpha CEP41 Centrosomal Protein 41 CerS1 Ceramid synthase 1 CHES1 Checkpoint suppressor 1 CID ITMS Collision induced dissociation Ion Trap Mobility Spectroscopy cJUN Jun Proto-Oncogene, AP-1 Transcription Factor Subunit Clip seq Cross-linking immunoprecipitation-high-throughput sequencing COX-2 Cyclooxygenase-2 CPLX2 Complexin-2 CRE Cis-regulatory element CSDC2 Cold Shock Domain Containing C2 CST5 Cystatin D CUGBP1 CUG-RNA binding protein 1 Da Dalton DAPI 4´,6-Diamidine-2´-phenylindole dihydrchloride DGCR8 DiGeorge Syndrome Critical Region 8 DIG Digoxigenin DM1 Myotonic dystrophy DMEM Dulbecco’s modified Eagle medium DMSO Dimethyl sulfoxide DNA Deoxyribonucleic acid DTT Dithiothreitol EBF1 Early B Cell Factor 1 EDTA Ethylenediaminetetraacetic acid eIF Eukaryotic Translation Initiation Factor EMT Epithelial-to-mesenchymal transition et al. And others FCS Fetal calf serum FDR False Discovery Rate FISH Fluorescence in situ hybridization FTMS Fourier transform mass analyzers GLOD4 Glyoxalase domain-containing protein 4 GRE GU-rich elements GRIPAP1 GRIP1 Associated Protein 1 GTF2E2 General transcription factor IIE subunit 2

Appendix __________________________________________________________________________

97

GTP Guanosine-5'-triphosphate h Hour HMGA2 High-mobility group AT-hook 2 HNF4A Hepatocyte Nuclear Factor 4 Alpha hnRNP E2 Heterogenous Nuclear Ribonucleoprotein E2 IL-1β Interleukin 1 beta IP Immunoprecipitation IPA Ingenuity pathway analysis Δ Knockdown LC-MS Liquid chromatography–mass spectrometry LEO1 LEO1 Homolog, Paf1/RNA Polymerase II Complex Component lncRNA /lnc Long non-coding RNA MACC1 Metastasis-Associated In Colon Cancer Protein 1 MAFK MAF BZIP Transcription Factor K MAPT Microtubule associated protein tau MFI2 Melanotransferrin min Minute miR Micro RNA mPGES-1 Microsomal prostaglandin E2 synthase 1 mRNA Messenger RNA MS Mass spectrometry mTOR Mechanistic Target of Rapamycin MYCBP MYC Binding Protein MYCN MYCN Proto-Oncogene MyD88 Myeloid differentiation primary response gene 88 NDUFS2 NADH- Ubiquinone Oxidoreductase Core Subunit S2 NMD Nonsense-mediated mRNA decay Nova-1 NOVA Alternative Splicing Regulator 1 NSCLC Non-small cell lung cancer nt Nucleotides oe Overexpression p38 Mitogen-Activated Protein Kinase 14 p70S6K Ribosomal protein S6 kinase beta-1 PAGE Polyacrylamide gel electrophoresis PAIP2 Poly(A) Binding Protein Interacting Protein 2 PARN Poly(A)-Specific Ribonuclease p-body Processing body PBS Phosphate buffered saline PCR Polymerase chain reaction PG Prostaglandin PGE2 Prostaglandin E2 pre-miR Precursor microRNA pri-miR Primary microRNA PTCSC3 Papillary Thyroid Carcinoma Susceptibility Candidate 3 PTPRU. Protein Tyrosine Phosphatase Receptor Type U Qki Quaking qRT-PCR Quantitative real time polymerase chain reaction RAN Ras-related nuclear protein RBD RNA-binding domain RBM23 RNA Binding Motif Protein 23 RBP RNA-binding protein RICTOR Rapamycin-Insensitive Companion Of MTOR RIP RNA immunoprecipitation RISC RNA-induced silencing complex

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98

RNA Ribonucleic acid RNP Ribonucleoprotein rpm Rounds per minute RRM RNA recognition motif s Second SCAI Suppressor Of Cancer Cell Invasion SDS Sodium dodecylsulfate SELEX Systematic Evolution of Ligands by EXponential Enrichment SEM Standard error of mean SETD7 SET Domain Containing 7, Histone Lysine Methyltransferase SLC39A6 Solute Carrier Family 39 Member 6 SMAD Mothers against decapentaplegic homolog Snail1 Snail Family Transcriptional Repressor 1 snRNP Small ribonucleoproteins SOD2 Superoxide dismutase 2, SRP Signal recognition particles t Time TDP-43 Transactive response DNA binding protein43 kDa TGF Transforming growth factor TMT Tandem mass tag TP53 Tumor protein 53 TRIF Toll-interleukin receptor domain containing adaptor-inducing interferon-β Tris Tris(hydroxymethyl)-aminomethan TSS Transcription start site TWIST1 Twist Family BHLH Transcription Factor 1 UBE2R2 Ubiquitin Conjugating Enzyme E2 R2 UBXN2B UBX Domain Protein 2B UTR Untranslated region V Voltage Wnt Wingless-Type MMTV Integration Site Family, Member 1 ZDHHC14 Zinc Finger DHHC-Type Containing 14 ZEB1 Zinc Finger E-Box Binding Homeobox 1 ZNF469-3 Zinc Finger Protein 469

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Supplementary data

Table 12. Top three upregulated proteins of the proteomics study.

Accession number

Description x-fold to control

ΔCUGBP1 soluble fraction

P04264 Keratin, type II cytoskeletal 1 OS=Homo sapiens GN=KRT1 PE=1 SV=6 - [K2C1_HUMAN]

20.43

Q5XKE5 Keratin, type II cytoskeletal 79 OS=Homo sapiens GN=KRT79 PE=1 SV=2 - [K2C79_HUMAN]

6.50

P13645 Keratin, type I cytoskeletal 10 OS=Homo sapiens GN=KRT10 PE=1

SV=6 - [K1C10_HUMAN] 3.80

ΔCUGBP1 microsomal fraction

F8WBR5 Isoform 3 of ADP-ribosylation factor-binding protein GGA1 OS=Homo sapiens GN=GGA1 - [GGA1_HUMAN]

3.47

P10606 Ubiquitin carboxyl-terminal hydrolase OS=Homo sapiens GN=UCHL1 PE=1 SV=1 - [D6R974_HUMAN]

3.15

E5RFX4 Isoform 2 of Glyoxalase domain-containing protein 4 OS=Homo

sapiens GN=GLOD4 - [GLOD4_HUMAN] 2.65

ΔmiR-574-5p soluble fraction

P04264 Keratin, type II cytoskeletal 1 OS=Homo sapiens GN=KRT1 PE=1

SV=6 - [K2C1_HUMAN] 3.25

A0A024QZ42 HCG1985580, isoform CRA_c OS=Homo sapiens GN=PDCD6 PE=1 SV=1 - [A0A024QZ42_HUMAN]

1.90

H0YD99 Eukaryotic translation initiation factor 4 gamma 2 OS=Homo sapiens GN=EIF4G2 PE=1 SV=1 - [H0YD99_HUMAN]

1.76

ΔmiR-574-5p microsomal fraction

P19971 Pre-mRNA-splicing factor SYF1 OS=Homo sapiens GN=XAB2

PE=1 SV=2 - [SYF1_HUMAN] 3.56

Q9NVX0-2 Thymidine phosphorylase OS=Homo sapiens GN=TYMP PE=1

SV=2 - [TYPH_HUMAN] 3.36

P00403 Isoform 2 of HAUS augmin-like complex subunit 2 OS=Homo sapiens GN=HAUS2 - [HAUS2_HUMAN]

2.91

miR-574-5p oe soluble fraction

Q5XKE5 Keratin, type II cytoskeletal 79 OS=Homo sapiens GN=KRT79 PE=1 SV=2 - [K2C79_HUMAN]

5.46

P04264 Keratin, type II cytoskeletal 1 OS=Homo sapiens GN=KRT1 PE=1

SV=6 - [K2C1_HUMAN] 5.12

H0YL43 Reticulocalbin-2 (Fragment) OS=Homo sapiens GN=RCN2 PE=1

SV=1 - [H0YL43_HUMAN] 2.67

miR-574-5p oe microsomal fraction

Q32P28-4 Copine-1 OS=Homo sapiens GN=CPNE1 PE=1 SV=1 - [A6PVH9_HUMAN]

5.93

Q92990-2 Protein S100 (Fragment) OS=Homo sapiens GN=S100A6 PE=1 SV=1 - [R4GN98_HUMAN]

5,70

F8VZJ2 Tetratricopeptide repeat protein 6 OS=Homo sapiens GN=TTC6

PE=4 SV=1 - [G3V435_HUMAN] 4,54

X-fold regulation compared to each control upon ΔCUGBP1, ΔmiR-574-5p and miR-574-5p oe in in

soluble and microsomal fraction of IL-1β-stimulated A549 cells.

Table 13. Top three downregulated proteins of the proteomics study.

Accession number

Description x-fold to control


P55060-3 Isoform 3 of Exportin-2 OS=Homo sapiens GN=CSE1L -

[XPO2_HUMAN] -5.99

Q9BQL6-3 Isoform 3 of Fermitin family homolog 1 OS=Homo sapiens GN=FERMT1 - [FERM1_HUMAN]

-4.15

Q15427 Splicing factor 3B subunit 4 OS=Homo sapiens GN=SF3B4 PE=1 SV=1 - [SF3B4_HUMAN]

-3.80


O00566 Leucine-rich repeat-containing protein 41 (Fragment) OS=Homo sapiens GN=LRRC41 PE=1 SV=1 - [A0A087WTI9_HUMAN]

-5.22

A0A0C4DFS8 Isoform 5 of CUGBP Elav-like family member 1 OS=Homo sapiens

GN=CELF1 - [CELF1_HUMAN] -3.09

F8VSL3 Ankyrin repeat and IBR domain-containing protein 1 OS=Homo sapiens GN=ANKIB1 PE=1 SV=3 - [AKIB1_HUMAN]

-3.07


Q9Y5J9 Mitochondrial import inner membrane translocase subunit Tim8 B OS=Homo sapiens GN=TIMM8B PE=1 SV=1 - [TIM8B_HUMAN]

-3.51

P62913-2 Isoform 2 of 60S ribosomal protein L11 OS=Homo sapiens GN=RPL11 - [RL11_HUMAN]

-3.18

O00425 Insulin-like growth factor 2 mRNA-binding protein 3 OS=Homo

sapiens GN=IGF2BP3 PE=1 SV=2 - [IF2B3_HUMAN] -2.95

Appendix __________________________________________________________________________

100


J3KS17 Isoform 2 of Nucleolar protein 9 OS=Homo sapiens GN=NOP9 - [NOP9_HUMAN]

-4.10

Q86U38-2 Calmodulin OS=Homo sapiens GN=CALM2 PE=1 SV=1 -

[F8WBR5_HUMAN] -3.00

F8WBR5 Isoform 3 of ADP-ribosylation factor-binding protein GGA1

OS=Homo sapiens GN=GGA1 - [GGA1_HUMAN] -2.87


P60903 Protein S100-A10 OS=Homo sapiens GN=S100A10 PE=1 SV=2 - [S10AA_HUMAN]

-4.48

P09525 Annexin A4 OS=Homo sapiens GN=ANXA4 PE=1 SV=4 - [ANXA4_HUMAN]

-3.86

Q8IVL5-2 Isoform 2 of Prolyl 3-hydroxylase 2 OS=Homo sapiens

GN=LEPREL1 - [P3H2_HUMAN] -2.43


P17302 Ninjurin-1 OS=Homo sapiens GN=NINJ1 PE=1 SV=2 - [NINJ1_HUMAN]

-7.23

Q9Y6A9 Transmembrane emp24 domain-containing protein 5 OS=Homo sapiens GN=TMED5 PE=1 SV=1 - [TMED5_HUMAN]

-5.16

O75127 Signal peptidase complex catalytic subunit SEC11A OS=Homo sapiens GN=SEC11A PE=1 SV=1 - [H0YKT4_HUMAN]

-4.96

X-fold regulation compared to each control upon ΔCUGBP1, ΔmiR-574-5p and miR-574-5p oe in in

soluble and microsomal fraction of IL-1β-stimulated A549 cells.

Table 14. IPA prediction of top five canonical pathways.

Name p-value Overlap


EIF2 Signaling 1.86E-45 55.7 % 103/185

Regulation of eIF4 and p70S6K Signaling 6.46E-31 50.6 % 78/154

Protein Ubiquitination Pathway 2.94E-27 39.0 % 99/254

tRNA Charging 2.38E-19 76.3 % 29/38

mTOR Signaling 2.92E-19 37.4 % 73/195


EIF2 Signaling 1.61E-48 69.7 % 129/185


Mitochondrial Dysfunction 5.21E-27 58.2 % 96/165

Regulation of eIF4 and p70S6K Signaling 2.30E-26 5 59.1 % 91/154

Oxidative Phosphorylation 8.96E-23 64.4 % 67/104


EIF2 Signaling 2.36E-45 55.7 % 103/185



tRNA Charging 2.55E-19 76.3 % 29/38

mTOR Signaling 3.37E-19 37.4 % 73/195


EIF2 Signaling 1.47E-48 69.7 % 129/185






EIF2 Signaling 2.45E-45 55.7 % 103/185



tRNA Charging 2.58E-19 76.3 % 29/38

mTOR Signaling 3.46E-19 37.4 % 73/195


EIF2 Signaling 1.51E-48 69.7 % 129/185





Table 15. IPA prediction of top five upstream regulators.

Name p-value


MYC 8.25E-72

HNF4A 2.34E-58

TP53 1.54E-57

MYCN 3.64E-50

MAPT 3.90E-42

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101

Table 16. Summary of all analyzed proteins. mPGES-1 ΔCUGBP1 ΔmiR-574-5p miR-574-5p oe

Soluble fraction

TMT Proteomics n.d. n.d. n.d.

IPA (Z-score) n.d. n.d. n.d.

Western blot n.d. n.d. n.d.

Microsomal fraction

TMT Proteomics 1.03 1.50 -1.07


Western blot 1.42 0.5 2

Smad2 ΔCUGBP1 ΔmiR-574-5p miR-574-5p oe

Soluble fraction

TMT Proteomics 1.1 -1.3 1.2


Western blot 0.5 1.6 1.2

Microsomal fraction


IPA (Z-score) 1.9 -2.1 n.d.


Smad3 ΔCUGBP1 ΔmiR-574-5p miR-574-5p oe

Soluble fraction

TMT Proteomics -2.1 1.0 -1.1



Microsomal fraction

TMT Proteomics 1.1 1.1 1.2

IPA (Z-score) 2.3 n.d. n.d.


NDUFS2 ΔCUGBP1 ΔmiR-574-5p miR-574-5p oe

Soluble fraction




Microsomal fraction





HNF4A 2.34E-82

CST5 6.86E-70

RICTOR 1.16E-53

MYC 8.00E-49

TP53 5.89E-43


MYC 1.54E-71

TP53 9.48E-58

HNF4A 1.39E-57

MYCN 4.80E-50

MAPT 7.02E-43


HNF4A 4.44E-82

CST5 6.03E-70

RICTOR 1.04E-53

MYC 6.60E-49

TP53 4.75E-43


MYC 1.71E-71

HNF4A 2.18E-58

TP53 2.18E-58

MYCN 5.02E-50

MAPT 7.31E-43


HNF4A 1.83E-82

CST5 6.29E-70

RICTOR 1.08E-53

MYC 7.04E-49

TP53 5.10E-43

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102

GLOD4 ΔCUGBP1 ΔmiR-574-5p miR-574-5p oe

Soluble fraction

TMT Proteomics -1.3 -1.0 -1.1



Microsomal fraction


IPA (Z-score) 2.7 n.d. n.d.


UBE2R2 ΔCUGBP1 ΔmiR-574-5p miR-574-5p oe

Soluble fraction



Western blot no information no information no information

Microsomal fraction




CPLX2 ΔCUGBP1 ΔmiR-574-5p miR-574-5p oe

Soluble fraction




Microsomal fraction

TMT Proteomics 2.0 -2.5 -1.7



CEP41 ΔCUGBP1 ΔmiR-574-5p miR-574-5p oe

Soluble fraction

TMT Proteomics 1.1 1.1 -1.2

IPA(Z-score) n.d. n.d. n.d.


Microsomal fraction




MAFK ΔCUGBP1 ΔmiR-574-5p miR-574-5p oe

Soluble fraction


IPA (Z-score) -1.3 -2.4 -0.7


Microsomal fraction


IPA (Z-score) -0.7 0.4 2.2


SETD7 ΔCUGBP1 ΔmiR-574-5p miR-574-5p oe

Soluble fraction

TMT Proteomics -1.40 1.02 1.01



Microsomal fraction




GRIPAP1 ΔCUGBP1 ΔmiR-574-5p miR-574-5p oe

Soluble fraction




Microsomal fraction




Appendix __________________________________________________________________________

103

SLC39A6 ΔCUGBP1 ΔmiR-574-5p miR-574-5p oe

Soluble fraction




Microsomal fraction




LEO1 ΔCUGBP1 ΔmiR-574-5p miR-574-5p oe

Soluble fraction




Microsomal fraction




GTF2E2 ΔCUGBP1 ΔmiR-574-5p miR-574-5p oe

Soluble fraction

TMT Proteomics -1.42 1.14 -1.51



Microsomal fraction




Regulation in TMT proteomics study, IPA (Z-score) prediction and Western blot analysis in soluble and

microsomal fraction upon ΔCUGBP1, ΔmiR-574-5p and miR-574-5p oe in IL-1β-stimulated A549 cells.

TMT Proteomics: TMT ratio compared to control sample; no information: not analyzed via Western blot

analysis; n.d.: not determined.

Table 17. Transcripts from bioinformatical 3’UTR analysis

Gene name

Ensemble transcript ID

CUGBP1 Binding motif

potential intron (nt)

Number of distinct binding sites

Fraction in TMT proteomics study

CEP41 ENST00000489512 TGTGTGTGT 199 2 microsomal

CEP41 ENST00000616628 TGTGTGTGT 199 2 microsomal

MAFK ENST00000343242 TGTGTGTG 384 2 microsomal

MAFK ENST00000403150 TGTGTGTG 384 2 microsomal

RBM23 ENST00000359890 TGTGTGTG 7513 2 microsomal

SETD7 ENST00000274031 TGTGTGTG 409 2 microsomal

UBE2R2 ENST00000263228 TGTGTGTG 592 2 microsomal

UBXN2B ENST00000399598 TGTGTGTG 551 2 microsomal

CPLX2 ENST00000359546 TGTGTGTG 1548 3 microsomal

SOD2 ENST00000538183 TGTGTGTGTGT 7537 2 soluble

CDCP1 ENST00000296129 TGTGTGTG 2382 2 soluble

Appendix __________________________________________________________________________

104

Curriculum vitae

Persönliche Daten

Name: Anne Caterina Emmerich, geboren am 20.11.1990 in Essen

Akademische Karriere

07/2016 – 01/2020 Promotion bei Dr. Meike Julia Saul, Prof. Dr. Beatrix Süß (Technische

Universität Darmstadt), sowie Prof. Dr. Dieter Steinhilber (Goethe-

Universität Frankfurt); Mitglied der Graduiertenschule FIRST und

Stipendiatin des Else-Kröner-Fresenius Graduiertenkollegs

10/2013 – 10/2015 Masterstudiengang „Molekulare Medizin“ an der Goethe- Universität

Frankfurt, Abschluss: Master of Science (1,4)

Masterarbeit am Institut für Allgemeine Pharmakologie und Toxikologie:

„The role of cAMP in expression of interleukin-22 by human T-cells“

10/2010 – 09/2013 Bachelorstudiengang „Biologie“ an der Johannes Gutenberg-Universität

Mainz, Abschluss: Bachelor of Science (1,6)

Bachelorarbeit am Institut für Zoologie, Abteilung Zell- und

Matrixbiologie: „Charakterisierung der humanen Metalloprotease

Tolloid-like 2“

08/2002 – 03/2010 Martin-von-Cochem-Gymnasium, Cochem (Mosel), Abitur (2,1)

Berufliche Erfahrung:

01/2016 – 07/2016 Industriepraktikum, Fresenius Medical Care, Bad Homburg von der

Höhe, „Biomedizinisches Projektmanagement“, Technology and

Innovation Management, Global R&D

Lehrtätigkeit:

2017 - 2019 Betreuung von Bachelor- und Masterstudenten

2017 - 2019 Tutorin des Bachelor Genetik Praktikums der TU-Darmstadt

Appendix __________________________________________________________________________

105

Konferenzen/Symposien:

RMU RNA Salon: Pulldown your RNA, Mainz

RMU RNA Salon: Ribonomics for every taste, Frankfurt a. M.

RMU RNA Salon: Genomics approaches in RNA Biology, Mainz

ECCPS Symposium: New goals in Cardio-Pulmonary Research, Bad Nauheim

Resolution of inflammation: mechanisms, mediators & bio-markers, Pescara, Italien

Publikationen:

Saul MJ, Baumann I, Bruno A, Emmerich AC, Wellstein J, Ottinger SM, Contursi A, Dovizio M,

Donnini S, Tacconelli S, Raouf J, Idborg H, Stein S, Korotkova M, Savai R, Terzuoli E, Sala G,

Seeger W, Jakobsson PJ, Patrignani P, Suess B, Steinhilber D. miR-574-5p as RNA decoy for

CUGBP1 stimulates human lung tumor growth by mPGES-1 induction. FASEB J. 2019

Saul MJ, Hegewald AB, Emmerich AC, Ossipova E, Vogel M, Baumann I, Kultima K, Lengqivst

J, Steinhilber D, Jakobsson PJ. Mass spectrometry-based proteomics approach characterizes

the dual functionality of miR-328 in monocytes. Front Pharmacol. 2019

Saul MJ, Emmerich AC, Steinhilber D, Süß B. Regulation of eicosanoid pathways by miRNAs.

Front Pharmacol. 2019

Emmerich AC, Wellstein J, Ossipova E, Baumann I, Lengqivst J, Kultima K, Jakobsson PJ,

Steinhilber D, Saul MJ. Proteomics-based characterization of miR-574-5p decoy to CUGBP1

suggests specificity for mPGES-1 regulation in human lung cancer cells. Front Pharmacol.,

2020.

Appendix __________________________________________________________________________

106

Ehrenwörtliche Erklärung

Ich erkläre hiermit ehrenwörtlich, dass ich die vorliegende Arbeit entsprechend den Regeln

guter wissenschaftlicher Praxis selbstständig und ohne unzulässige Hilfe Dritter angefertigt

habe.

Sämtliche aus fremden Quellen direkt oder indirekt übernommenen Gedanken sowie

sämtliche von Anderen direkt oder indirekt übernommenen Daten, Techniken und Materialien

sind als solche kenntlich gemacht. Die Arbeit wurde bisher bei keiner anderen Hochschule zu

Prüfungszwecken eingereicht.

Darmstadt, den 08.01.2020

…………………………………….

Anne Caterina Emmerich

Teile dieser Arbeit wurden bereits publiziert in:

Saul MJ, Baumann I, Bruno A, Emmerich AC, Wellstein J, Ottinger SM, Contursi A, Dovizio M,

Donnini S, Tacconelli S, Raouf J, Idborg H, Stein S, Korotkova M, Savai R, Terzuoli E, Sala G,

Seeger W, Jakobsson PJ, Patrignani P, Suess B, Steinhilber D. miR-574-5p as RNA decoy for

CUGBP1 stimulates human lung tumor growth by mPGES-1 induction. FASEB J. 2019

Emmerich AC, Wellstein J, Ossipova E, Baumann I, Lengqivst J, Kultima K, Jakobsson PJ,

Steinhilber D, Saul MJ. Proteomics-based characterization of miR-574-5p decoy to CUGBP1

suggests specificity for mPGES-1 regulation in human lung cancer cells. Front Pharmacol.,

2020.

Appendix __________________________________________________________________________

107

Danksagungen

Als erstes möchte ich mich bei Dr. Meike Saul ganz herzlich bedanken für die Aufnahme in

ihre Gruppe und die Möglichkeit meine Doktorarbeit durchzuführen. Nicht nur für die gute

Betreuung und vielen fachlichen Diskussionen, sondern auch, dass du immer ein offenes Ohr

hattest und mit Rat und Tat zur Seite standest.

Vielen Dank auch an Prof. Dr. Beatrix Süß und Prof. Dr. Dieter Steinhilber für ihre

Unterstützung und die zahlreichen konstruktiven Diskussionen ob in Frankfurt, Darmstadt oder

bei den Summer- und Winterschools.

Des Weiteren möchte ich mich bei der Else-Kröner-Fresenius Graduiertenschule für die

Finanzierung bedanken und ganz besonders auch bei Dr. Brigitte Held.

Ein ganz großes Dankeschön geht auch an alle ehemaligen und derzeitigen Mitglieder der

Arbeitsgruppen Süß und Weigand. Durch die tolle Arbeitsatmosphäre und Kollegialität habe

ich mich immer gut aufgehoben gefühlt. Besonderen Dank an Sandra, Stephen, Theresa,

Jamina und Britta. Es hat Spaß gemacht mit euch allen zusammenzuarbeiten.

Ebenfalls vielen Dank an unsere gar nicht mehr so kleine AG Saul. Besonders möchte ich

mich bei Anett, Julia, Leon und Eva bedanken, für den tollen Zusammenhalt, die lustigen

Mittagspausen und die allgemeine Labor-Madness. Ich werde noch lange an all die verrückten

Pavarotti- und Dorie-Momente denken. Ich hoffe, dass es jetzt nicht all zu still wird. Jetzt muss

einfach einer von euch durchs Labor tanzen und dabei sein Eis verschütten, ok?

Danke für die sehr schöne Zeit!

Mein besonderer Dank gilt auch meiner Eltern Norbert und Trudi, die mich immer unterstützt

haben - vom Bachelor- über Masterstudium bis hin zum Doktor. Ohne euch wäre das nicht

möglich gewesen!

Schließlich möchte ich mich bei Andi bedanken, der in der gesamten Zeit einfach immer für

mich da war und mir Sicherheit und Rückhalt gegeben hat. Ohne dich hätte ich das niemals

geschafft! Danke für Alles!

Documents

Characterization of miR-574-5p decoy to CUGBP1 in human ......Emmerich, Anne Caterina: Characterization of miR-574-5p decoy to CUGBP1 in human lung cancer cells using a mass spectrometry